System, apparatus and methods for data collection and assessing outcomes

CROSS-REFERENCE TO RELATED APPLICATIONS

0001This application claims the benefit under 35 USC §119(e) of U.S. Provisional Application No. 61/831,075 titled “SYSTEM, APPARATUS AND METHODS FOR DATA COLLECTION AND ASSESSING OUTCOMES,” filed Jun. 4, 2013, the disclosure of which application is herein incorporated by reference.

0002This application is related to U.S. patent application Ser. No. 13/844,386, filed on Mar. 15, 2013, the disclosure of which application is herein incorporated by reference.

INTRODUCTION

0003Individual data gathered by a Proteus system (therapy, behavior, physiology) together with publically available data may influence overall health risk and health maintenance of the individual. Such data may include individual demographics, prevalence of disease in a particular geography, proximity to health care service centers, performance (outcomes) associated to HCPs (Health care Provider's) in local, proximity to businesses that might influence healthy behavior (health clubs, restaurants, fast-food, grocery stores, parks, etc).

0004By combining the data, risk can be properly adjusted for the specific patient situation and appropriate resource can be applied to manage the risk appropriately.

0005The aggregate patient health risk can be more effectively determined by combining Proteus system data (therapeutic regimen and adherence, behavior, engagement, and physiology) together with one or more data elements.

0006The data listed above may also be used without a Proteus system data to provide an independent risk-score. By combining the data, risk can be properly adjusted for the specific patient situation and appropriate resource can be applied to manage the risk appropriately. The data might be sourced from a combination of direct capture, public data sources and/or privately available systems/sources.

SUMMARY

0007In one aspect, a computer-implemented method is disclosed. The computer-implemented method comprises receiving, by a computer system, ingestible event marker (IEM) system information from a receiver worn by a subject, the IEM system information comprising information associated with ingestion of medication by the subject, wherein the receiver is configured to communicate with the computer system; receiving, by the computer system, contextual information associated with the subject; and calculating, by the computer system, a composite risk score based on the IEM system information and the contextual information associated with the subject.

0008In one aspect, a computer-implemented method is disclosed. The computer-implemented method comprises receiving, by a computer system, daily data from a subject; calculating, by the computer system, the daily data into a dominant circadian pattern; tracking, by the computer system, daily variations between the daily data and the dominant circadian pattern; and calculating, by the computer system, a distribution of the daily variations; and quantifying, by the computer system, the distribution of the daily variations by differential entropy.

0009In one aspect, a computer-implemented method is disclosed. The computer-implemented method comprises receiving, by a computer system, daily data from a subject; applying, by the computer system, principal component analysis to the received daily data to determine a dominant mode; determining, by the computer system, a variation between the daily data and the dominant mode; and determining, by the computer system, how many samples of the daily data are above a predetermined threshold.

0010The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, aspects, and features described above, further aspects, aspects, and features will become apparent by reference to the drawings and the following detailed description.

FIGURES

0011The novel features of the aspects described herein are set forth with particularity in the appended claims. The aspects, however, both as to organization and methods of operation may be better understood by reference to the following description, taken in conjunction with the accompanying drawings as follows.

0012<figref idref="DRAWINGS">FIG. 1</figref> illustrates a data characterization diagram depicting data types according to one aspect.

0013<figref idref="DRAWINGS">FIG. 2</figref> is a graphical representation of average posture per three hour period by individual according to one aspect.

0014<figref idref="DRAWINGS">FIG. 3A</figref> illustrates a data processing flow diagram employed in determining the classification of each patient according to one aspect.

0015<figref idref="DRAWINGS">FIG. 3B</figref> illustrates data fields employed in determining the classification of each patient according to one aspect.

0016<figref idref="DRAWINGS">FIG. 4</figref> is a graphical illustration of posture data for a healthy control individual according to one aspect.

0017<figref idref="DRAWINGS">FIG. 5</figref> is a graphical representation of an Eigenday example for a healthy control individual according to one aspect.

0018<figref idref="DRAWINGS">FIG. 6</figref> is a detail view of a section of the Eigenday graphical representation of <figref idref="DRAWINGS">FIG. 5</figref> showing detail graphs of the Eigenday <b>1</b> and Eigenday <b>2</b>.

0019<figref idref="DRAWINGS">FIG. 7</figref> is a differentially shaded coded graphical representation of primary diurnal cycle—posture Eigenday for Eigenday <b>1</b> shown in <figref idref="DRAWINGS">FIGS. 5-6</figref> by individual.

0020<figref idref="DRAWINGS">FIG. 8</figref> is a graphical representation of the CV of the daily RMS change by individual of the first Eigenday <b>1</b> posture according to one aspect.

0021<figref idref="DRAWINGS">FIG. 9</figref>, there is shown a graphical representation of an example Eigenday <b>1</b> plotted along with data obtained from a healthy control individual according to one aspect.

0022<figref idref="DRAWINGS">FIG. 10</figref> is a magnified view of the indicated section of the graph shown in <figref idref="DRAWINGS">FIG. 9</figref> over a 24-hour period.

0023<figref idref="DRAWINGS">FIG. 11</figref> is a graphical representation showing the residuals calculated by subtracting the baseline Eigenday <b>1</b> from each individual's multi-day data set.

0024<figref idref="DRAWINGS">FIG. 12</figref> is a graphical representation of unfiltered data for a healthy control individual according to one aspect.

0025<figref idref="DRAWINGS">FIG. 13</figref> is a graphical representation of filtered data for a healthy control individual according to one aspect.

0026<figref idref="DRAWINGS">FIG. 14</figref> is a diagram illustrating a data field correlation matrix according to one aspect where the vertical scale to the right of the matrix is a differentially shaded gradient representing the level of correlation.

0027<figref idref="DRAWINGS">FIG. 15</figref> is a graphical representation target group data showing the amount of time the target group was above the threshold according to one aspect.

0028<figref idref="DRAWINGS">FIG. 16</figref> is a graphical representation of control group data showing the amount of time the control group was above the threshold according to one aspect.

0029<figref idref="DRAWINGS">FIG. 17</figref> is a graphical representation of a leave one out (LOO) classification testing according to one aspect showing the time above threshold for schizophrenic vs. healthy control groups.

0030<figref idref="DRAWINGS">FIG. 18</figref> is a graphical representation of distribution entropy classification based on features of the posture data distribution based on a high entropy example from a schizophrenic group.

0031<figref idref="DRAWINGS">FIG. 19</figref> is a distribution histogram of posture data for the high entropy example.

0032<figref idref="DRAWINGS">FIG. 20</figref> is a graphical representation of a distribution entropy classification based on features of the data distribution based on a low entropy example from a control group.

0033<figref idref="DRAWINGS">FIG. 21</figref> is a distribution histogram of posture data for the low entropy example.

0034<figref idref="DRAWINGS">FIG. 22</figref> is a graphical representation of a LOO classification testing according to one aspect showing the distribution entropy for schizophrenic vs. healthy control groups.

0035<figref idref="DRAWINGS">FIG. 23</figref> is a diagram illustrating data characterization for expanded data classes according to one aspect.

0036<figref idref="DRAWINGS">FIG. 24</figref> is a graphical representation of breadth vs. depth framing of data values according to one aspect.

0037<figref idref="DRAWINGS">FIG. 25</figref> is a graphical representation patient acceptance vs. patient effort framing of data values according to one aspect.

0038<figref idref="DRAWINGS">FIG. 26</figref> is a graphical representation of the amount of data needed for framing of data values according to one aspect.

0039<figref idref="DRAWINGS">FIG. 27</figref> is a graphical representation of a leave one out (LOO) classification testing showing time above threshold comparing schizophrenic vs. healthy controls according to one aspect.

0040<figref idref="DRAWINGS">FIG. 28</figref> is a graphical representation of posture data set data distribution according to one aspect.

0041<figref idref="DRAWINGS">FIG. 29</figref> is a graphical representation of posture data set residual distribution according to one aspect.

0042<figref idref="DRAWINGS">FIG. 30</figref> is a diagram illustrating the continuous patient journey with episodic intervention between home, hospital, and clinic according to one aspect.

0043<figref idref="DRAWINGS">FIG. 31</figref> is a diagram illustrating a comprehensive risk-based product offering that captures all patients throughout their journey according to one aspect.

0044<figref idref="DRAWINGS">FIG. 32</figref> is a diagram of several graphical representations to assist visualization of longitudinal data according to one aspect.

0045<figref idref="DRAWINGS">FIG. 33</figref> is a graphical representation of the comparative length of data sets showing the number of good days of data by individual according to one aspect.

0046<figref idref="DRAWINGS">FIG. 34</figref> is a graphical representation of posture entropy metric enhancements for fixed segment length entropy (FSLE) according to one aspect.

0047<figref idref="DRAWINGS">FIG. 35</figref> is a diagram of several graphical representations of an FSLE example depicting abnormalities in circadian posture patterns according to one aspect.

0048<figref idref="DRAWINGS">FIG. 36</figref> is a distribution plot indicating the FSLE value and the P-value where the P-value represents the portion of the distribution that is above a given FSLE value.

0049<figref idref="DRAWINGS">FIG. 37</figref> is a graphical representation of posture FSLE P-values by subject group according to one aspect.

0050<figref idref="DRAWINGS">FIG. 38</figref> is a distribution curve of an individual's FSLE risk score.

0051<figref idref="DRAWINGS">FIG. 39</figref> is a graphical representation of subject posture FSLE risk score according to one aspect.

0052<figref idref="DRAWINGS">FIG. 40</figref> is a diagram of several graphical representations of posture FSLE used as input to PICO score for Drilldown #1 according to one aspect.

0053<figref idref="DRAWINGS">FIG. 41</figref> is a diagram of several graphical representations of posture FSLE used as input to PICO score for Drilldown #2 according to one aspect.

0054<figref idref="DRAWINGS">FIG. 42</figref> is a graphical representation of posture FSLE risk ranking across subject groups according to one aspect.

0055<figref idref="DRAWINGS">FIG. 43</figref> is a diagram illustrating therapeutic data for additional Proteus indication of clinical outcomes (PICO) risk input according to one aspect.

0056<figref idref="DRAWINGS">FIG. 44</figref> is a diagram of several graphical representations of the inclusion of IEM data into the PICO score according to one aspect.

0057<figref idref="DRAWINGS">FIG. 45</figref> illustrates a diagram of an IEM ingestion matrix characterization of therapy taking according to one aspect.

0058<figref idref="DRAWINGS">FIG. 46</figref> is graphical representations of the FSLE risk score versus daily adherence according to one aspect.

0059<figref idref="DRAWINGS">FIG. 47</figref> is graphical representations of the FSLE risk score dose daily adherence according to one aspect.

0060<figref idref="DRAWINGS">FIG. 48</figref> is a diagram illustrating inter-week (global) variability score intrinsic dimensionality according to one aspect.

0061<figref idref="DRAWINGS">FIG. 49</figref> is a diagram for processing whitened principal component analysis (WPCA) data to determine how many samples are above the intrinsic dimensionality threshold.

0062<figref idref="DRAWINGS">FIG. 50</figref> is a diagram illustrating the intra-week (local) variability scores average deviation from daily pattern according to one aspect.

0063<figref idref="DRAWINGS">FIG. 51</figref> is diagram for processing the WPCA data <b>376</b> into average standard deviation data according to tone aspect.

0064<figref idref="DRAWINGS">FIG. 52</figref> is a graphical representation of global versus local [(ASTD*ADIFF)]*0.5 according to one aspect.

0065<figref idref="DRAWINGS">FIG. 53</figref> is a graphical representation of a data matrix (e.g., posture circadian pattern) of a first subject (Customer <b>1</b>) according to one aspect.

0066<figref idref="DRAWINGS">FIG. 54</figref> is a graphical representation of combined taking adherence and dose adherence according to one aspect.

0067<figref idref="DRAWINGS">FIG. 55</figref> is a graphical representation of a data matrix (e.g., posture circadian pattern) of a second subject (Customer <b>2</b>) according to one aspect.

0068<figref idref="DRAWINGS">FIG. 56</figref> is a graphical representation of combined taking adherence and dose adherence according to one aspect.

0069<figref idref="DRAWINGS">FIG. 57</figref> is a graphical representation of distribution of patient scores according to one embodiment for both subjects (Customer <b>1</b> and Customer <b>2</b>) according to one aspect.

0070<figref idref="DRAWINGS">FIG. 58</figref> is a graphical representation showing the correlation between circadian score and missed dose adherence according to one aspect.

0071<figref idref="DRAWINGS">FIG. 59</figref> is a diagram illustrating a comprehensive risk and intervention strategy according to one aspect.

0072<figref idref="DRAWINGS">FIG. 60</figref> is a graphical representation of a joint circadian pattern (JCP) risk distribution by data set where the horizontal pattern represents risk score and the vertical axis represents the cumulative distribution function for the various identified groups according to one aspect.

0073<figref idref="DRAWINGS">FIG. 61</figref> is a graphical representation of a pharmacokinetic dose (PKD) risk by data set according to one aspect.

0074<figref idref="DRAWINGS">FIG. 62</figref> is a graphical representation of the overall risk distribution by data set according to one aspect.

0075<figref idref="DRAWINGS">FIG. 63</figref> is a graphical representation of a partial trend analysis of a patient (e.g., Lifenote Subject #3) with a partial trend fit using a maximum standard error (MSE) of 171 according to one aspect.

0076<figref idref="DRAWINGS">FIG. 64</figref> is a graphical representation of a partial trend analysis of the same patient (e.g., Lifenote Subject #3) with a partial trend fit using a maximum MSE of 85 according to one aspect.

0077<figref idref="DRAWINGS">FIG. 65</figref> is a graphical representation of a partial trend analysis of the same patient (e.g., Lifenote Subject #3) with a partial trend fit using a maximum MSE of 17 according to one aspect.

0078<figref idref="DRAWINGS">FIG. 66</figref> is a diagram illustrating a partial trend analysis of a Lifenote data set according to one aspect where the horizontal axis represents the day index and the vertical axis represents the subject index.

0079<figref idref="DRAWINGS">FIG. 67</figref> is diagram illustrating a partial trend analysis of a Helius data set according to one aspect where the horizontal axis represents the day index and the vertical axis represents the subject index.

0080<figref idref="DRAWINGS">FIG. 68</figref> is a graphical representation of a 2-D scatter plot comparing the final risk trend magnitude and the risk trend slope over the last week of data according to one aspect.

0081<figref idref="DRAWINGS">FIG. 69</figref> is a graphical representation of an overall risk profile for an individual according to one aspect.

0082<figref idref="DRAWINGS">FIG. 70</figref> is conceptual illustration of a system comprising an adhesive patch to receive a signal from an IEM to confirm that the user swallowed the medication according to one aspect.

0083<figref idref="DRAWINGS">FIG. 71</figref> is a block diagram representation of an event indicator system with dissimilar metals positioned on opposite ends, according to one aspect.

0084<figref idref="DRAWINGS">FIG. 72</figref> is a block diagram representation of the event indicator system with dissimilar metals positioned on the same end and separated by a non-conducting material, according to one aspect.

0085<figref idref="DRAWINGS">FIG. 73</figref> shows ionic transfer or the current path through a conducting fluid when the event indicator system of <figref idref="DRAWINGS">FIG. 71</figref> is in contact with conducting liquid and in an active state according to one aspect.

0086<figref idref="DRAWINGS">FIG. 74</figref> shows an exploded view of the surface of dissimilar materials of <figref idref="DRAWINGS">FIG. 73</figref>, according to one aspect.

0087<figref idref="DRAWINGS">FIG. 75</figref> shows the event indicator system of <figref idref="DRAWINGS">FIG. 73</figref> with a pH sensor unit, according to one aspect.

0088<figref idref="DRAWINGS">FIG. 76</figref> is a block diagram illustration of the control device used in the system of <figref idref="DRAWINGS">FIGS. 26 and 27</figref>, according to one aspect.

0089<figref idref="DRAWINGS">FIG. 77</figref> is a functional block diagram of a demodulation circuit that performs coherent demodulation that may be present in a receiver according to one aspect.

0090<figref idref="DRAWINGS">FIG. 78</figref> illustrates a functional block diagram for a beacon module within a receiver, according to one aspect.

0091<figref idref="DRAWINGS">FIG. 79</figref> is a block diagram of the different functional modules that may be present in a receiver, according to one aspect.

0092<figref idref="DRAWINGS">FIG. 80</figref> is a block diagram of a receiver, according to one aspect.

0093<figref idref="DRAWINGS">FIG. 81</figref> provides a block diagram of a high frequency signal chain in a receiver, according to one aspect.

0094<figref idref="DRAWINGS">FIG. 82</figref> provides a diagram of how a system that includes a signal receiver and an ingestible event marker may be employed according to one aspect.

0095<figref idref="DRAWINGS">FIG. 83</figref> illustrates one aspect of a personal communication system for obtaining patient physiological and IEM system information into a computer system according to one aspect.

DESCRIPTION

0096Aspects of the present disclosure include combining individual data gathered by a Proteus system (therapy, behavior, physiology, e.g., as described in greater detail in Appendix A) together with publically available data that might influence overall health risk. Data might include individual demographics, prevalence of disease in a particular geography, proximity to health care service centers, performance (outcomes) associated to HCPs in local, proximity to businesses that might influence healthy behavior (health clubs, restaurants, fast-food, grocery stores, parks, etc).

0097By combining the data, risk can be properly adjusted for the specific patient situation and appropriate resource can be applied to manage the risk appropriately.

0098The aggregate patient health risk can be more effectively determined by combining Proteus system data (therapeutic regimen and adherence, behavior, engagement, and physiology) together with one or more of the following data elements: <ul id="ul0001" list-style="none"><li id="ul0001-0001" num="0000"><ul id="ul0002" list-style="none"><li id="ul0002-0001" num="0099">Demographic (age, ethnicity, etc)</li><li id="ul0002-0002" num="0100">Socioeconomic status</li><li id="ul0002-0003" num="0101">Education level</li><li id="ul0002-0004" num="0102">Living situation</li><li id="ul0002-0005" num="0103">Family situation</li><li id="ul0002-0006" num="0104">Credit history</li><li id="ul0002-0007" num="0105">Prevalence of disease in a particular geography</li><li id="ul0002-0008" num="0106">Proximity to health care service centers</li><li id="ul0002-0009" num="0107">Location</li><li id="ul0002-0010" num="0108">Performance (outcomes) associated to HCPs (doctors, nurses, hospitals, care centers, etc) servicing the local patient population</li><li id="ul0002-0011" num="0109">Geographic composition of businesses that might influence healthy behavior (health clubs, restaurants, fast-food, grocery stores, parks, etc).</li><li id="ul0002-0012" num="0110">Relative HCP load (patients to HCP ratio)</li><li id="ul0002-0013" num="0111">Diagnostic data (e.g. blood pressure measurements in hypertension)</li><li id="ul0002-0014" num="0112">Genomic context (i.e. genomic predisposition to disease)</li><li id="ul0002-0015" num="0113">Medical history</li><li id="ul0002-0016" num="0114">Medication possession ratio</li><li id="ul0002-0017" num="0115">Work situation and schedule</li><li id="ul0002-0018" num="0116">Social environment</li></ul></li></ul>

0117The data listed above may also be used without a Proteus system data to provide an independent risk-score. By combining the data, risk can be properly adjusted for the specific patient situation and appropriate resource can be applied to manage the risk appropriately. The data might be sourced from a combination of direct capture, public data sources and/or privately available systems/sources.

0118In one aspect a fusion technique employing Multivariate and Bayesian statistical techniques may be used to determine the combined or composite risk score.

0119For a basic understanding of the operational details of the Proteus IEM and receiver (patch) system, the reader is directed to <figref idref="DRAWINGS">FIGS. 71-83</figref> and the accompanying description within the present disclosure, which illustrate and describe a typical Proteus system comprising an ingestible event marker (IEM) (described in connection with <figref idref="DRAWINGS">FIGS. 71-76</figref>) and a receiver “patch” system (described in connection with <figref idref="DRAWINGS">FIGS. 77-83</figref>. For conciseness and clarity of disclosure, the description now turns to a description of various aspects of a system, apparatus, and method for data collection and assessing outcomes employing a Proteus system. Once therapeutic and physiological data is obtained by the Proteus patch system, the data can be communicated to any processing engine configured to analyze the data. For example, the Proteus patch system data can be communicated to a backend computer processing system comprising a server or other suitable computing apparatus, a database, and communication interface as shown and described in connection with <figref idref="DRAWINGS">FIG. 82</figref>, for example. In one aspect, the backend server can be configured as a specialized machine to receive the Proteus patch system data and compute the desired results. These and other aspects will now be described in connection with the figures.

Patch-Derived Data Analytics for Improved Patient Outcomes

0120One question to be determined is whether feedback from patch-derived data analytics can be utilized to improve outcomes in target patients, such as those suffering from major depressive disorder, bipolar, and/or schizophrenia. In one aspect, the system can identify and quantify individuals at risk using patch-derived data. In a predictive model, the system is configured to determine the likelihood of a patient being hospitalized, being non-compliant, or developing a “new” condition. State monitoring and feedback data can be utilized to determine titration issues, optimization of drug effect, or worsening condition of patient state.

0121<figref idref="DRAWINGS">FIG. 1</figref> illustrates a data characterization diagram <b>100</b> depicting data types according to one aspect. A first data type <b>102</b> includes therapeutic data, i.e., drug taking compliance and timing, and physiological data, i.e., electrocardiogram (ECG), Temperature, Activity, Posture, etc. The data regime <b>106</b> is depth <b>110</b> and the data source <b>108</b> is a patch receiver as described below in connection with <figref idref="DRAWINGS">FIGS. 71 and 77-83</figref>. Traditionally, data <b>104</b> was subjectively determined from patient-reported moods, side effects, symptoms, etc. and patient history. Thus, traditionally the data regime <b>106</b> is historical <b>114</b> and the data source <b>108</b> is a patient records database <b>116</b>.

0122TABLE 1 below, depicts the value of data types in a patient lifecycle management process. The value from a pure data perspective is indicated in the table as Low, Medium, or High.

0000<tables id="TABLE-US-00001" num="00001"><table frame="none" colsep="0" rowsep="0"><tgroup align="left" colsep="0" rowsep="0" cols="2"><colspec colname="offset" colwidth="154pt" align="left" /><colspec colname="1" colwidth="63pt" align="center" /><thead><row><entry /><entry namest="offset" nameend="1" rowsep="1">TABLE 1</entry></row></thead><tbody valign="top"><row><entry /><entry namest="offset" nameend="1" align="center" rowsep="1" /></row><row><entry /><entry>Patient/Records</entry></row></tbody></tgroup><tgroup align="left" colsep="0" rowsep="0" cols="4"><colspec colname="offset" colwidth="63pt" align="left" /><colspec colname="1" colwidth="91pt" align="center" /><colspec colname="2" colwidth="35pt" align="center" /><colspec colname="3" colwidth="28pt" align="left" /><tbody valign="top"><row><entry /><entry>Patch-Derived</entry><entry /><entry>Patient</entry></row></tbody></tgroup><tgroup align="left" colsep="0" rowsep="0" cols="5"><colspec colname="1" colwidth="63pt" align="left" /><colspec colname="2" colwidth="42pt" align="left" /><colspec colname="3" colwidth="49pt" align="left" /><colspec colname="4" colwidth="35pt" align="left" /><colspec colname="5" colwidth="28pt" align="left" /><tbody valign="top"><row><entry>Patient State</entry><entry>Therapeutic</entry><entry>Physiological</entry><entry>Subjective</entry><entry>History</entry></row><row><entry namest="1" nameend="5" align="center" rowsep="1" /></row><row><entry>Pre-Treatment</entry><entry>Low</entry><entry>High</entry><entry>High</entry><entry>High</entry></row><row><entry>Assessment</entry></row><row><entry>Treatment Titration</entry><entry>High</entry><entry>High</entry><entry>High</entry><entry>Low</entry></row><row><entry>Management/</entry><entry>High</entry><entry>High</entry><entry>Medium</entry><entry>Low</entry></row><row><entry>Maintenance</entry></row><row><entry namest="1" nameend="5" align="center" rowsep="1" /></row></tbody></tgroup></table></tables>

0123One question associated with patch-derived data analysis is whether patch-derived data fields can be effective for patient identification and classification. Systems according to the present disclosure provide high classification accuracies using patch-derived data fields and data fusion techniques can be used to enhance performance. The data fields used in the patch-derived data analysis include, without limitation, acceleration, posture, steps, temperature, and heart rate—median and standard deviation. An aggregated data field may be employed to determine whether the patent is asleep.

0124<figref idref="DRAWINGS">FIG. 2</figref> is a graphical representation <b>120</b> of average posture per three hour period by individual according to one aspect. The horizontal axis represents the day of the week and the vertical axis represents individual patient index. Along the vertical axis, the patients are classified into three groups where Group I patients 1-16 are classified as schizophrenic, Group II patients 17-28 are classified as Bipolar, and Group III patients 29-47 are classified as healthy control. Immediately to the right of the graph <b>120</b>, a vertical differentially shaded gradient scale <b>122</b> represents the individual's posture angle in degrees. The graph <b>120</b> provides a visualization of 5-minute data averaged over 3-hour periods. The resting/upright diurnal cycles <b>124</b> are visually obvious even though there is significant variance between the individuals. Optimally, each individual would have a separate baseline.

0125<figref idref="DRAWINGS">FIG. 3A</figref> illustrates a data processing flow diagram <b>130</b> and <figref idref="DRAWINGS">FIG. 3B</figref> illustrates the corresponding data fields <b>150</b> employed in determining the classification of each patient according to one aspect. As shown in the flow diagram <b>130</b> of <figref idref="DRAWINGS">FIG. 3A</figref>, the system conditions <b>132</b> the patient data using seven data fields, for example. The system then computes <b>134</b> diurnal cycle “Eigenday” and stores the results into corresponding seven data fields. The system calculates 136 deviations “residuals” and stores the result in seven corresponding data fields. The system then whitens <b>138</b> the residuals and stores the results into seven corresponding data fields <b>150</b> shown in <figref idref="DRAWINGS">FIG. 3B</figref>. After the residuals are whitened <b>138</b>, the system optionally can filter <b>140</b> the residuals, storing the results into seven corresponding data fields. The system fuses <b>142</b> all the data from the seven fields <b>150</b> into a single field <b>152</b>. Finally, the system classifies <b>144</b> the data based on the data fusion <b>142</b>, represented by <b>154</b>

0126<figref idref="DRAWINGS">FIG. 4</figref> is a graphical illustration <b>160</b> of posture data for a healthy control individual according to one aspect. The horizontal axis represents hour of day where 24=midnight and the vertical axis represents posture angle in degrees where zero degrees is resting posture and

0000−90 degrees is completely upright. <figref idref="DRAWINGS">FIG. 5</figref> is a graphical representation <b>162</b> of an Eigenday example for a healthy control individual according to one aspect. Similar to the graphical representation of <figref idref="DRAWINGS">FIG. 4</figref>, the horizontal axis represents hour of day where 24=midnight and the vertical axis represents posture angle in degrees. The graphical representation of <figref idref="DRAWINGS">FIG. 5</figref>, depicts two Eigendays, a first Eigenday <b>1</b><b>164</b> captures the dominant pattern features. A second, Eigenday <b>2</b><b>166</b> captures more subtle corrections due to multi-day variations such as sleeping in on weekends. <figref idref="DRAWINGS">FIG. 6</figref> is a detail view <b>168</b> of a section <b>6</b> of the Eigenday graphical representation of <figref idref="DRAWINGS">FIG. 5</figref> showing detail graphs of the Eigenday <b>1</b><b>164</b> and Eigenday <b>2</b><b>166</b>. As shown in <figref idref="DRAWINGS">FIGS. 4-6</figref>, many days of posture data can be decomposed into patterns to calculate the primary diurnal cycle baseline. These patterns, referred to herein as Eigendays, are ordered according to the dominance of the pattern in the data.

0127<figref idref="DRAWINGS">FIG. 7</figref> is a differentially shaded coded graphical representation <b>170</b> of primary diurnal cycle—posture Eigenday for Eigenday <b>1</b> shown in <figref idref="DRAWINGS">FIGS. 5-6</figref> by individual. The horizontal axis represents time of day, the left vertical axis represents the individual index, and the right vertical axis represents posture angle in degrees in differentially shaded coded pattern analogous to a thermal image pattern. The patient class is arranged in three groups: Group I patients 1-16 classified as schizophrenic, Group II patients 17-28 classified as Bipolar, and Group III patients 29-47 classified as healthy control. The dominant posture Eigenday for all individuals is shown in 5-minute data collection interval. The differentially shaded code is configured such that warm differentially shaded portion indicates a laying down posture and cool differentially shaded portion indicates an upright posture.

0128<figref idref="DRAWINGS">FIG. 8</figref> is a graphical representation <b>172</b> of the CV of the daily RMS change by individual of the first Eigenday <b>1</b> posture according to one aspect. The horizontal axis represents the Day Index and the vertical axis represents the CV (RMSD). The graph <b>172</b> illustrates that an adequate baseline may be obtained by increasing the number of days in which data are gathered. As shown in <figref idref="DRAWINGS">FIG. 8</figref>, as more days of data are added to the analysis, the diurnal pattern <b>174</b> becomes more accurate. Nevertheless, beyond a certain point the gains in accuracy are small. The graph <b>172</b> shows that the convergence slows greatly after a few weeks, e.g., around day 50. The values represented along the vertical axis measure how much the Eigenday <b>1</b> value changes due to the inclusion of an extra day of data. The data used to plot the graph <b>172</b> was obtained from a longer-term collection from transplant patients so that convergence could be observed on a long time scale.

0129<figref idref="DRAWINGS">FIGS. 9-11</figref> are graphical representations of residuals or quantized deviations from a baseline for a healthy control individual. In each of the graphs shown in <figref idref="DRAWINGS">FIGS. 9-11</figref>, the horizontal axis represents the hour of day where 24=midnight and the vertical axis represents posture angle in degrees. Turning now to <figref idref="DRAWINGS">FIG. 9</figref>, there is shown a graphical representation <b>180</b> of an example Eigenday <b>1</b><b>182</b> plotted along with data <b>184</b> obtained from a healthy control individual according to one aspect. <figref idref="DRAWINGS">FIG. 10</figref> is a magnified view of the indicated section <b>10</b> of the graph <b>180</b> shown in <figref idref="DRAWINGS">FIG. 9</figref> over a 24-hour period. <figref idref="DRAWINGS">FIG. 11</figref> is a graphical representation <b>186</b> showing the residuals <b>188</b> calculated by subtracting the baseline Eigenday <b>1</b><b>182</b> from each individual's multi-day data <b>184</b> set. The resulting residual graph <b>186</b> captures events like resting during the day and sleeping late.

0130<figref idref="DRAWINGS">FIG. 12</figref> is a graphical representation <b>190</b> of unfiltered data for a healthy control individual according to one aspect. <figref idref="DRAWINGS">FIG. 13</figref> is a graphical representation of filtered data <b>192</b> for a healthy control individual according to one aspect. In <figref idref="DRAWINGS">FIGS. 12-13</figref>, the horizontal axis represents the hour of day where 24=midnight and the vertical axis represents posture angle in degrees. As shown in <figref idref="DRAWINGS">FIGS. 12-13</figref>, filtering the residuals enables examination of behavior over long time scales. However, in some cases short-term residual fluctuation includes more noise than useful information. Accordingly, boxcar filtering of the data can reveal persistent long-term residuals which may contain more information for classification. As shown in <figref idref="DRAWINGS">FIG. 12</figref>, in the unfiltered data all the short-term deviations are evident. As shown in <figref idref="DRAWINGS">FIG. 13</figref>, in the filtered data the long-term deviations are illuminated. The filtered data can reveal where the individual was in a laying down position more than usual or in an upright position more than usual.

0131<figref idref="DRAWINGS">FIG. 14</figref> is a diagram <b>194</b> illustrating a data field correlation matrix according to one aspect where the vertical scale to the right of the matrix is a differentially shaded gradient representing the level of correlation. Data fusion entails combing multiple data fields into one detection/classification metric. In the illustrated example the data fields are provided along the rows and columns of the data field correlation matrix and may include, for example, the following data fields: 1) Acceleration; 2) Posture; 3) Asleep; 4) Steps; 5) Med HR; 6) Std HR; and 7) Temperature. Correlations between data fields are detrimental thus fusing two correlated data sources will emphasize the correlated feature. There are significant correlations between our data fields that must be considered.

0132Data fusion is the combination of multiple data types into one metric. Fusing multiple data residuals into an “effective residual” can be represented, for example, by the following expression:

0000<br /><i>D</i>_M({right arrow over (<i>x</i>)})=√{square root over (<i>{right arrow over (x)}</i>^T<i>C</i>⁻¹{right arrow over (<i>x</i>)})}

0133Where D_Mis the “Mahalanobis Distance” and C is the correlation matrix. This expression accounts for the correlations between the data fields to create a “single” residual-type variable that can be used seamlessly with the classification schemes as described in more detail hereinbelow.

0134A first classification Method 1 classifies the percentage of time the residuals are above a threshold. The time above threshold classification provides an optimal residual threshold that is selected from the training group. The percentage time the residual resides above that threshold is used for classification. <figref idref="DRAWINGS">FIG. 15</figref> is a graphical representation <b>196</b> target group data showing the amount of time the target group was above the threshold according to one aspect. <figref idref="DRAWINGS">FIG. 16</figref> is a graphical representation <b>198</b> of control group data showing the amount of time the control group was above the threshold according to one aspect. In the graphs of <figref idref="DRAWINGS">FIGS. 15-16</figref>, the horizontal axis represents the data index and the vertical axis represents the fused residual. In the illustrated example, the data from the Target and Control Groups is the fusion of the acceleration and temperature data fields. It will be appreciated that other data fields can be fused as shown in the data field correlation matrix of <figref idref="DRAWINGS">FIG. 14</figref>.

0135<figref idref="DRAWINGS">FIG. 17</figref> is a graphical representation <b>200</b> of a leave one out (LOO) classification testing according to one aspect showing the time above threshold for schizophrenic vs. healthy control groups. The horizontal axis represents variables included and the vertical axis represents the LOO classification accuracies (%). The graph <b>200</b> represents the training set accuracy <b>202</b> and the LOO test accuracy <b>204</b>. In the illustrated example, the LOO classification is the percentage of time above threshold, filtered over a 24-hour filter window for schizophrenic vs. healthy control groups. Of the training set <b>202</b> and LOO test <b>204</b> accuracies shown, the maximum Max LOO test accuracy is ˜85.3%. The methodology employs acceleration, posture, and temperature data fields jointly. It will be appreciated that other data fields such as sleep, steps, median HR and standard deviation HR can be employed without limitation.

0136A second classification Method 2 classifies distribution entropy, which is classifying based on features of the data distribution. <figref idref="DRAWINGS">FIG. 18</figref> is a graphical representation <b>206</b> of distribution entropy classification based on features of the posture data distribution based on a high entropy example from a schizophrenic group. The horizontal axis represents data index and the vertical axis represents whitened residual. To the right of <figref idref="DRAWINGS">FIG. 18</figref>, <figref idref="DRAWINGS">FIG. 19</figref> is a distribution histogram <b>208</b> of posture data for the high entropy example. The horizontal axis represents whitened residual and the vertical axis represents the number of occurrences. Turning to <figref idref="DRAWINGS">FIG. 20</figref>, there is illustrated a graphical representation <b>210</b> of a distribution entropy classification based on features of the data distribution based on a low entropy example from a control group. To the right of <figref idref="DRAWINGS">FIG. 20</figref>, <figref idref="DRAWINGS">FIG. 21</figref> is a distribution histogram <b>212</b> of posture data for the low entropy example. The horizontal axis represents whitened residual and the vertical axis represents the number of occurrences. The entropy of a distribution can be used to quantify inherently different distributions. This can be effective as a classifier if different patient classes tend to have different residual distributions.

0137<figref idref="DRAWINGS">FIG. 22</figref> is a graphical representation <b>214</b> of a leave one out (LOO) classification testing according to one aspect showing the distribution entropy for schizophrenic vs. healthy control groups. The horizontal axis represents variables included and the vertical axis represents the LOO classification accuracies (%). The graph represents the training set accuracy <b>202</b> and the LOO test accuracy <b>204</b>. Unfiltered whitened residuals have a maximum LOO test accuracy <b>204</b> of 88.24%. The classification employs only the posture variable. Fusing multiple variables does not provide improved classification in this case. The methodology may employ acceleration, temperature, sleep, steps, median HR and standard deviation HR can be employed without limitation.

0138In conclusion, high classification accuracies were achieved. Deviations from a unique baseline were calculated for each individual. The deviations or “residuals” appear to possess significant classification power. In addition, the patch-derived data fields worked well for classification. Patch-derived data fields perform as well or better than an aggregate data field for identification and classification of individuals in the study. Finally, data fusion was shown to enhance performance. Fusion of patch-derived data fields provided utility for improving patient classification.

0139<figref idref="DRAWINGS">FIG. 23</figref> is a diagram <b>216</b> illustrating data characterization for expanded data classes according to one aspect. The expanded data sets are in addition to the Therapeutic, Physiological, Subjective, and Patient History data classes shown in <figref idref="DRAWINGS">FIG. 1</figref>. In <figref idref="DRAWINGS">FIG. 23</figref>, the expanded data classes include Behavioral Engagement, Contextual, and Content. The regime is breadth and the source is phone-derived. The Behavioral Engagement data class may include application use frequency, feature use, and problem solving. The Contextual data class includes sociability (Calls, Texts sent, and Calendar), location diversity (Wi-Fi and Bluetooth environments), ignoring of calls, texts, emails. The Content data class includes tone in communications (Voice, Text, and Emails) and web surfing behavior.

0140<figref idref="DRAWINGS">FIG. 24</figref> is a graphical representation <b>218</b> of breadth vs. depth framing of data values according to one aspect. The horizontal axis represents depth (insight) ranging from low to high and the vertical axis represents breadth (coverage) ranging from low to high. The Therapeutic and Physiological data classes are patch-derived, whereas the contextual, content, and behavioral engagement data classes are phone-derived. The subjective and patient history data classes are patient-derived. As can be seen in <figref idref="DRAWINGS">FIG. 24</figref>, the phone-derived data types add complementary analytical coverage of patient state to the patch-derived and patient data. As also shown in <figref idref="DRAWINGS">FIG. 24</figref>, the patch-derived data types are high depth (insight) and low breadth (coverage). The contextual and content phone-derived data types are high breadth (coverage) and low depth (insight). The patient-derived data types are both low depth (insight) and low breadth (coverage). The behavioral engagement phone-derived data types strike a balance between depth (insight) and breadth (coverage). Key data types include therapeutic (Thp), physiologic (Phy), behavioral/engagement (B/E), contextual (Ctx), content (Cnt), subjective (Sbj), and patient history (PH), among others, for example.

0141<figref idref="DRAWINGS">FIG. 25</figref> is a graphical representation <b>220</b> patient acceptance vs. patient effort framing of data values according to one aspect. The horizontal axis represents patient acceptance and the vertical axis represents patient effort. As in <figref idref="DRAWINGS">FIG. 24</figref>, the Therapeutic and Physiological data classes are patch-derived, whereas the contextual, content, and behavioral engagement data classes are phone-derived. The patient acceptance of the phone-derived data type collection could be problematic although it requires little effort. As can be seen in <figref idref="DRAWINGS">FIG. 25</figref>, the content phone-derived data type has hard patient acceptance and low patient effort. The contextual phone-derived data type has medium acceptance and low effort. The physiologic patch-derived data type has medium acceptance and medium effort. The patient history patient-derived data type has easy acceptance and low effort. The therapeutic patch-derived data type has easy acceptance but slightly higher effort than the physiologic data type. The behavioral engagement phone-derived data type has easy acceptance and medium effort. The subjective patient-derived data type has easy acceptance and high effort. Key data types include therapeutic (Thp), physiologic (Phy), behavioral/engagement (B/E), contextual (Ctx), content (Cnt), subjective (Sbj), and patient history (PH), among others, for example.

0142<figref idref="DRAWINGS">FIG. 26</figref> is a graphical representation <b>222</b> of the amount of data needed fro framing of data values according to one aspect. The horizontal axis represents days/users ranging from a week to 3 months and the vertical axis represents number of users ranging from 1 to 100. As shown in <figref idref="DRAWINGS">FIG. 26</figref>, the therapeutic, subjective, and physiologic data types are hypothesis driven and have low time and low number of users. The contextual, content, and behavioral/engagement data types are between hypothesis and data driven over a month with a medium number of users. The physiological data types are data driven over three months and have a large number of users. The phone-derived data analyses will be more hypothesis-driven than the data-driven physiological analysis. Key data types include therapeutic (Thp), physiologic (Phy), behavioral/engagement (B/E), contextual (Ctx), content (Cnt), subjective (Sbj), and patient history (PH), among others, for example.

0143TABLE 2 below, depicts the value of the expanded data types in a patient lifecycle management process. The phone-derived data adds value in the management/maintenance patient state.

0000<tables id="TABLE-US-00002" num="00002"><table frame="none" colsep="0" rowsep="0"><tgroup align="left" colsep="0" rowsep="0" cols="4"><colspec colname="offset" colwidth="63pt" align="left" /><colspec colname="1" colwidth="56pt" align="center" /><colspec colname="2" colwidth="49pt" align="center" /><colspec colname="3" colwidth="49pt" align="center" /><thead><row><entry /><entry namest="offset" nameend="3" rowsep="1">TABLE 2</entry></row></thead><tbody valign="top"><row><entry /><entry namest="offset" nameend="3" align="center" rowsep="1" /></row><row><entry /><entry>Patch-Derived</entry><entry /><entry>Patient/Records</entry></row></tbody></tgroup><tgroup align="left" colsep="0" rowsep="0" cols="3"><colspec colname="offset" colwidth="91pt" align="left" /><colspec colname="1" colwidth="105pt" align="left" /><colspec colname="2" colwidth="21pt" align="left" /><tbody valign="top"><row><entry /><entry>Behav-</entry><entry>Pa-</entry></row></tbody></tgroup><tgroup align="left" colsep="0" rowsep="0" cols="5"><colspec colname="1" colwidth="91pt" align="left" /><colspec colname="2" colwidth="28pt" align="left" /><colspec colname="3" colwidth="49pt" align="center" /><colspec colname="4" colwidth="28pt" align="left" /><colspec colname="5" colwidth="21pt" align="left" /><tbody valign="top"><row><entry>Pa-</entry><entry>ioral</entry><entry>Phone-Derived</entry><entry /><entry>tient</entry></row></tbody></tgroup><tgroup align="left" colsep="0" rowsep="0" cols="8"><colspec colname="1" colwidth="35pt" align="left" /><colspec colname="2" colwidth="28pt" align="left" /><colspec colname="3" colwidth="28pt" align="left" /><colspec colname="4" colwidth="28pt" align="left" /><colspec colname="5" colwidth="28pt" align="left" /><colspec colname="6" colwidth="21pt" align="left" /><colspec colname="7" colwidth="28pt" align="left" /><colspec colname="8" colwidth="21pt" align="left" /><tbody valign="top"><row><entry>tient</entry><entry>Thera-</entry><entry>Physio-</entry><entry>Engage-</entry><entry>Contex-</entry><entry>Con-</entry><entry>Subjec-</entry><entry>His-</entry></row><row><entry>State</entry><entry>peutic</entry><entry>logical</entry><entry>ment</entry><entry>tual</entry><entry>tent</entry><entry>tive</entry><entry>tory</entry></row><row><entry namest="1" nameend="8" align="center" rowsep="1" /></row><row><entry>Pre-Treat-</entry><entry>Low</entry><entry>High</entry><entry>Low</entry><entry>Low</entry><entry>Low</entry><entry>High</entry><entry>High</entry></row><row><entry>ment As-</entry></row><row><entry>sessment</entry></row><row><entry>Treatment</entry><entry>High</entry><entry>High</entry><entry>Low</entry><entry>Low</entry><entry>Low</entry><entry>High</entry><entry>Low</entry></row><row><entry>Titration</entry></row><row><entry>Manage-</entry><entry>High</entry><entry>High</entry><entry>High</entry><entry>High</entry><entry>High</entry><entry>Medium</entry><entry>Low</entry></row><row><entry>ment/</entry></row><row><entry>Main-</entry></row><row><entry>tenance</entry></row><row><entry namest="1" nameend="8" align="center" rowsep="1" /></row></tbody></tgroup></table></tables>

0144<figref idref="DRAWINGS">FIG. 27</figref> is a graphical representation <b>224</b> of a leave one out (LOO) classification testing showing time above threshold comparing schizophrenic vs. healthy controls. The data was gathered over a 36-hour filter window. The maximum LOO test accuracy is 91.18% using only acceleration and temperature data fields. The horizontal axis represents variables included and the vertical axis represents LOO classification accuracies (%). Additional data fields include posture, sleep, steps, median HR, and standard deviation HR, without limitation.

0145<figref idref="DRAWINGS">FIG. 28</figref> is a graphical representation <b>226</b> of posture data set data distribution according to one aspect. The horizontal axis represents variable value and the vertical axis represents occurrences. <figref idref="DRAWINGS">FIG. 29</figref> is a graphical representation <b>228</b> of posture data set residual distribution according to one aspect. The horizontal axis represents residual value and the vertical axis represents occurrence. Residuals are calculated by subtracting the baseline Eigenday from each individual's multi-day data set. The patch-derived posture data compiled over the data set shows a bi-modal distribution corresponding to resting/upright states. The residual distribution over the same data is unimodal. This deviation from normal is used for classification.

PICO: Proteus Indication of Clinical Outcomes—A Risk-Based Approach Based on Therapeutic and Physiologic Data

0146<figref idref="DRAWINGS">FIG. 30</figref> is a diagram illustrating <b>230</b> the continuous patient journey with episodic intervention between home <b>232</b>, hospital <b>234</b>, and clinic <b>236</b> according to one aspect. The key question is how to identify who is in need of attention and apply the right resources to the right person at the right time. <figref idref="DRAWINGS">FIG. 31</figref> is a diagram <b>238</b> illustrating a comprehensive risk-based product offering that captures all patients throughout their journey according to one aspect. The patient population <b>240</b> is assessed <b>242</b> based on patient population data such as medical history, demographics, socio-economic, HCP performance, geography, education, behavioral history, among other data. A risk-based intervention class includes standard protocol <b>244</b>, low touch intervention <b>246</b>, and medium touch intervention <b>248</b>. Subsequently, the data is assessed <b>250</b> based on individual data is performed, such as therapy, behavior, physiology, genomic, diagnostic, among other assessment techniques. Feedback may be provided from the medium touch intervention <b>248</b> to the low touch intervention <b>246</b> and from the low touch intervention <b>246</b> to the standard protocol <b>244</b>. From the assessment stage <b>250</b> the patient proceeds to the high touch intervention <b>252</b> stage with feedback to the assessment stage <b>250</b> if necessary. Thus, a risk based approach based on complex algorithms can be applied to composite data sets to determine PICO scores. PICO scores are somewhat analogous to the Fair-Issac Corporation (FICO) score employed as the global standard for assessing consumer credit risk that drives millions of credit decisions daily. The FICO scoring system is used by 90 of the 100 largest financial institutions in the U.S., and all the 100 largest U.S. credit card issuers. FICO fraud systems protect more than two billion card accounts worldwide and 23 million consumers in the US pay for real-time access to their FICO scores.

0147Accordingly, in one aspect, the present disclosure provides a patch-derived data analytics system to contribute to constructing a health equivalent of a FICO score referred to herein as a PICO score, for example. The PICO score provides an assessment of patient risk and stability across therapeutic areas and enables automated data-driven clinical decision making. This system can provide effective drug therapy management and improved therapeutic program effectiveness at reduced cost. In one aspect, metrics may be derived that are suitable for quantifying the patch-derived data as input to the PICO score. The PICO score system may be employed to predict whether the patient is likely to be hospitalized or non-compliant or developing “new” condition. The PICO score system may provide patient state monitoring and feedback, titration issues, optimization of drug effect, or worsening condition of patient state.

0148<figref idref="DRAWINGS">FIG. 32</figref> is a diagram <b>252</b> of several graphical representations to assist visualization of longitudinal data according to one aspect. A first graph <b>254</b> illustrates a patient's circadian pattern based on posture where the horizontal axis represents the time of day and the vertical axis represents the number of days. The first graph <b>254</b> is differentially shaded coded pattern analogous to a thermal image pattern. The dark or black areas indicate that the patient is in a supine position, the light blue areas indicate that the patient is in a sitting position, the red areas indicate that the patient is in a standing position, and the white areas indicate that the patient was not wearing the patch. A second graph <b>256</b> immediately below the first graph <b>254</b> illustrates the patient's average circadian pattern where the horizontal axis represents time of day and the vertical axis represents degrees. A third graph <b>258</b> illustrates the medication that the patient is taking representing pill taking behavior where the horizontal axis represents the time of day and the vertical axis represents the number of days. A fourth graph <b>260</b> immediately below illustrates the timing adherence in time of day versus number of pills taken where the horizontal axis represents the time of day and the vertical axis represents the number of pills taken. A fifth graph <b>262</b> illustrates how many pills were taken in a day representing pill taking adherence where the horizontal axis represents the number of pills taken in a day and the vertical axis represents the number of days. The white spaces <b>264</b> denote the dose taken and the asterisk <b>266</b> denotes that the patch was not worn and thus excluded the session from the percentage (%). Accordingly, at a glance, one can obtain a visualization of long term data based on the patient's circadian pattern based on posture and medication taking behavior.

0149The disclosure now turns briefly to an overview of the patch-derived data analytics for patient risk assessment over two phases. In phase 1, high classification accuracies were achieved with patch-derived data. The “Residual Entropy” method enabled patient classification accuracy of 88.24% as previously described in connection with <figref idref="DRAWINGS">FIG. 22</figref> for N=34 subjects (15 neuropsych/19 healthy volunteers. The data fusion of multiple physiological parameters was shown to provide performance enhancement for some cases. In phase 2, the residual entropy metric was enhanced for input into PICO score. For this study the patch-derived posture data was used and refined to be time-window specific and agnostic to data set length and sample rate. This provided strong utility to catch subject instabilities. The therapeutic patch-derived data (IEM) studied for utility towards PICO score provided two degrees of freedom identified to enable risk quantification and of potential correlation between therapeutic and physiological abnormalities in the data.

0150In addition to the original phase 1 data set, three new data sets were used and there were some key differences between the data sets. The new data sets consisted entirely for institutionalized subjects with severe health issues. Phase 1 subjects were not institutionalized and the subjects that were not in the neuropsych group were healthy volunteers. The new data sets were collected with an updated version of the patch where many of the sampling rates were higher. These additional data sets are summarized in TABLE 3.

0000<tables id="TABLE-US-00003" num="00003"><table frame="none" colsep="0" rowsep="0"><tgroup align="left" colsep="0" rowsep="0" cols="6"><colspec colname="1" colwidth="35pt" align="left" /><colspec colname="2" colwidth="42pt" align="center" /><colspec colname="3" colwidth="42pt" align="center" /><colspec colname="4" colwidth="28pt" align="center" /><colspec colname="5" colwidth="35pt" align="center" /><colspec colname="6" colwidth="35pt" align="center" /><thead><row><entry namest="1" nameend="6" rowsep="1">TABLE 3</entry></row><row><entry namest="1" nameend="6" align="center" rowsep="1" /></row><row><entry>Data</entry><entry>#</entry><entry /><entry /><entry>Other</entry><entry>Institu-</entry></row><row><entry>Summary</entry><entry>of Subjects</entry><entry>Neuropsych</entry><entry>Healthy</entry><entry>Ailments</entry><entry>tionalized</entry></row><row><entry namest="1" nameend="6" align="center" rowsep="1" /></row></thead><tbody valign="top"><row><entry /></row></tbody></tgroup><tgroup align="left" colsep="0" rowsep="0" cols="6"><colspec colname="1" colwidth="35pt" align="left" /><colspec colname="2" colwidth="42pt" align="char" char="." /><colspec colname="3" colwidth="42pt" align="center" /><colspec colname="4" colwidth="28pt" align="center" /><colspec colname="5" colwidth="35pt" align="center" /><colspec colname="6" colwidth="35pt" align="center" /><tbody valign="top"><row><entry>Phase 1</entry><entry>35</entry><entry>X</entry><entry>X</entry><entry /><entry /></row><row><entry>Alpha</entry><entry>15</entry><entry>X</entry><entry /><entry>X</entry><entry>X</entry></row><row><entry>Hbbread</entry><entry>12</entry><entry /><entry /><entry>X</entry><entry>X</entry></row><row><entry>Hbbrim</entry><entry>12</entry><entry /><entry /><entry>X</entry><entry>X</entry></row><row><entry namest="1" nameend="6" align="center" rowsep="1" /></row></tbody></tgroup></table></tables>

0151<figref idref="DRAWINGS">FIG. 33</figref> is a graphical representation <b>270</b> of the comparative length of data sets showing the number of good days of data by individual according to one aspect. The horizontal axis represents the subject index and the vertical represents the number of good days. The data sets Phase 1, Alpha, Hbbbread, and Hbbrim are graphed as histograms. The newer data sets Alpha, Hbbbread, and Hbbrim are much longer in general than the Phase 1 data set. Statistical metrics can be dependent on the length of the data set and can lead to the development of a new metric related to the Phase 1 but allowing for disparate data set lengths.

0152The PICO entropy metric quantifies the variability in the subject's circadian patterns where the dominant circadian patterns are calculated from subject's historical data. The higher the entropy of the variations from the baseline circadian patterns the larger the subject's variability. With reference to <figref idref="DRAWINGS">FIG. 4</figref>, the daily data can be decomposed into dominant circadian patterns as shown in <figref idref="DRAWINGS">FIG. 6</figref>. The daily variations that are tracked as shown in <figref idref="DRAWINGS">FIG. 9</figref> can be illustrated as distributions of variations quantified by differential entropy as shown in <figref idref="DRAWINGS">FIGS. 19 and 21</figref>, for example.

0153<figref idref="DRAWINGS">FIG. 34</figref> is a graphical representation <b>272</b> of posture entropy metric enhancements for fixed segment length entropy (FSLE) according to one aspect. The horizontal axis represents the day index and the vertical axis FSLE value. One set of data is for Alpha Subject #4—Neuropsych and another set of data is for Alpha Subject #6—Diabetes type 2. Previously a single entropy value for whole data set was calculated. The FSLE technique provides a series of entropy values over fixed time intervals and allows temporal assessment of patient stability.

0154<figref idref="DRAWINGS">FIG. 35</figref> is a diagram <b>274</b> of several graphical representations of an FSLE example depicting abnormalities in circadian posture patterns according to one aspect. The diagram <b>274</b> illustrates that spikes in FSLE values are indicative of days when the subject's posture pattern is highly variable. A first graph <b>276</b> illustrates the posture circadian pattern for Alpha subject #14. The red areas of the differentially shaded coded pattern indicate that the patient is in standing upright position and the dark blue areas indicate that the patient is in a supine position. Intermediate differentially shaded pattern indicates that the patient is in an intermediate position between upright and supine. The horizontal axis represents time of day and the vertical axis represents the number of days. Immediately below the first graph <b>276</b> is a second graph <b>278</b> illustrating the average circadian pattern where the horizontal axis still represents time of day and the vertical axis represents the number of days. Immediately to the right of the first graph <b>276</b> is a third graph <b>280</b> depicting FSLE where the horizontal axis represents a daily FSLE vale and the vertical axis represents the number of days. The third graph <b>280</b> can show a robust capture of aberrations in the circadian posture pattern. Immediately below the third graph <b>280</b> is a fourth graph <b>282</b> depicting the FSLE distribution where the horizontal axis represents the FSLE value and the vertical axis represents the number of occurrences.

0155Posture FSLE distributions may be obtained by subject group. The FSLE distributions from aggregated subject group values can be plotted and different therapeutic areas can be clustered together. <figref idref="DRAWINGS">FIG. 36</figref> is a distribution plot <b>284</b> indicating the FSLE value and the P-value where the P-value represents the portion of the distribution that is above a given FSLE value. <figref idref="DRAWINGS">FIG. 37</figref> is a graphical representation <b>286</b> of posture FSLE P-values by subject group according to one aspect. The horizontal axis represents FSLE value and the vertical axis represents group distribution P-value. The subject groups are Phase 1 Neuropsych, Alpha Neuropsych, Phase 1 Healthy, Alpha Other Ailments, Hbbread Other Ailments, and Hbbrim Other Ailments. The two target neuropsych groups have the highest P-values of entire set. The healthy Phase 1 group has much lower overall P-values. The three 3 institutionalized groups with other ailments cluster together in the middle.

0156The posture FSLE can be used as an input into the PICO score. <figref idref="DRAWINGS">FIG. 38</figref> is a distribution curve <b>288</b> of an individual's FSLE risk score. As shown in <figref idref="DRAWINGS">FIG. 38</figref>, an individual subject's FSLE distribution curve <b>288</b> can be quantified as a scalar for input into a PICO score where the FSLE Score is chosen where the P-Value=0.159 and one standard deviation above the distribution mean. <figref idref="DRAWINGS">FIG. 39</figref> is a graphical representation <b>289</b> of subject posture FSLE risk score according to one aspect. The horizontal axis represents the subject index and the vertical axis represents the FSLE score. As illustrative examples, the subsequent disclosure drills down into two specific cases Drilldown #1 for Alpha subject #11 and Drilldown #2 for Hbbrim subject #9.

0157<figref idref="DRAWINGS">FIG. 40</figref> is a diagram <b>290</b> of several graphical representations of posture FSLE used as input to PICO score for Drilldown #1 according to one aspect. The diagram <b>290</b> illustrates the Drilldown #1 for Alpha subject #11 a Neuropsych with lower risk score and a relatively stable posture circadian pattern. A first graph <b>292</b> illustrates the posture circadian pattern for the Alpha subject #11 where the horizontal axis represents time of day and the vertical axis represents the number of days. The red areas of the differentially shaded coded pattern indicate that the patient is in standing upright position and the dark blue areas indicate that the patient is in a supine position. Intermediate differentially shaded pattern indicates that the patient is in an intermediate position between upright and supine. The horizontal axis represents time of day and the vertical axis represents the number of days. Immediately below the first graph <b>292</b> is a second graph <b>294</b> illustrating the average circadian pattern where the horizontal axis still represents time of day and the vertical axis represents the number of days. Immediately to the right of the first graph <b>292</b> is a third graph <b>296</b> depicting FSLE where the horizontal axis represents a daily FSLE vale and the vertical axis represents the number of days. The third graph <b>296</b> shows a robust capture of aberrations in the circadian posture pattern. Immediately below the third graph <b>296</b> is a fourth graph <b>298</b> depicting the FSLE distribution where the horizontal axis represents the FSLE value and the vertical axis represents the number of occurrences. The daily FSLE captured a brief period of unstable circadian patterns.

0158<figref idref="DRAWINGS">FIG. 41</figref> is a diagram <b>300</b> of several graphical representations of posture FSLE used as input to PICO score for Drilldown #2 according to one aspect. The diagram <b>300</b> illustrates the Drilldown #2 for Hbbrim subject #9 a hypercholesterolemia, hypertension, ischemic heart disease with higher risk score. A first graph <b>302</b> illustrates the posture circadian pattern for the Hbbrim subject #9 where the horizontal axis represents time of day and the vertical axis represents the number of days. The red areas of the differentially shaded coded pattern indicate that the patient is in standing upright position and the dark blue areas indicate that the patient is in a supine position. Intermediate differentially shaded pattern indicates that the patient is in an intermediate position between upright and supine. Immediately below the first graph <b>302</b> is a second graph <b>304</b> illustrating the average circadian pattern where the horizontal axis still represents time of day and the vertical axis represents the number of days. Immediately to the right of the first graph <b>302</b> is a third graph <b>306</b> depicting FSLE where the horizontal axis represents a daily FSLE vale and the vertical axis represents the number of days. The third graph can show a robust capture of aberrations in the circadian posture pattern. Immediately below the third graph <b>306</b> is a fourth graph <b>308</b> depicting the FSLE distribution where the horizontal axis represents the FSLE value and the vertical axis represents the number of occurrences. The daily FSLE shows a period of stable circadian patterns characterized by low FSLE values and as the circadian patterns become unstable the FSLE values increase accordingly.

0159<figref idref="DRAWINGS">FIG. 42</figref> is a graphical representation <b>310</b> of posture FSLE risk ranking across subject groups according to one aspect. The graph <b>310</b> represents the posture FSLE risk quartiles by group where the horizontal axis represents risk quartile and the vertical axis represents the number of subjects in the group. The blue bars <b>312</b> represent the Neurpsych group, the green bars <b>314</b> represent the Other Ailments group, and the Brown <b>316</b> bars represent the Healthy groups. The groups <b>312</b>, <b>314</b>, <b>316</b> are ranked according to risk from left to right: low risk, marginal risk, moderate risk, and high risk. Ranking of subject risk shows the utility of FSLE as a risk measure. Neuropsych subjects strongly tend towards high risk. Healthy control subjects are strongly low risk. “Other ailment” groups spread more evenly with Moderate Risk the highest.

0160<figref idref="DRAWINGS">FIG. 43</figref> is a diagram <b>320</b> illustrating therapeutic data for additional PICO risk input according to one aspect. IEM data is used as medication adherence risk indicator. Predictive medication adherence provides substantial added value to the overall PICO risk score. A first graph <b>322</b> illustrates pill taking behavior where the horizontal axis represents time of day and the vertical axis represents the number of days. Immediately below the first graph <b>322</b> is a second graph <b>324</b> that illustrates timing adherence where the horizontal axis represents the time of day and the vertical axis represents the number of pills. The second graph <b>324</b> indicates a tight distribution over the data period, which shows relatively good temporal adherence. Immediately to the right of the first graph <b>322</b> is a third graph <b>326</b> illustrating taking adherence where the horizontal axis represents the number of pills taken in a day and the vertical axis represents the number of days. As shown in the third graph <b>326</b>, the white horizontal bars <b>328</b> indicate days where the medication appears to have been missed. As shown in <figref idref="DRAWINGS">FIG. 44</figref> there are two degrees of freedom daily adherence and temporal (timing) adherence. Daily adherence refers to the number of pills that are prescribed versus the number that are actually taken per day. Temporal (timing) adherence refers to the timing regularity with which the medications are taken. Temporal adherence is a good indicator of the patient's discipline and reliability with respect to medication adherence.

0161<figref idref="DRAWINGS">FIG. 44</figref> is a diagram <b>330</b> of several graphical representations of the inclusion of IEM data into the PICO score according to one aspect. The diagram <b>330</b> illustrates that high FSLE values coincide with IEM abnormalities. A first graph <b>332</b> illustrates the posture circadian pattern for the Alpha subject #14 where the horizontal axis represents time of day and the vertical axis represents the number of days. The red areas of the differentially shaded coded pattern indicates that the patient is in standing upright position and the dark blue areas indicate that the patient is in a supine position. Intermediate differentially shaded pattern indicates that the patient is in an intermediate position between upright and supine. Immediately below the first graph <b>332</b> is a second graph <b>334</b> illustrating the average circadian pattern where the horizontal axis still represents time of day and the vertical axis represents the number of days. Immediately to the right of the first graph <b>332</b> is a third graph <b>336</b> depicting FSLE where the horizontal axis represents a daily FSLE vale and the vertical axis represents the number of days. Immediately below the third graph <b>338</b> is a fourth graph <b>340</b> depicting the FSLE distribution where the horizontal axis represents the FSLE value and the vertical axis represents the number of occurrences. Immediately to the right of the third graph <b>326</b> is a fifth graph <b>340</b> depicting pill taking behavior where the horizontal axis represents the time of day and the vertical axis represents the number of days. Immediately below the fifth graph <b>340</b> is a sixth graph <b>342</b> depicting timing adherence where the horizontal axis represents the time of day and the vertical axis represents the number of pills. In the illustrated graph six <b>342</b>, the diagram shows good consistency on timing adherence. Immediately to the right of the fifth graph <b>340</b> is a seventh graph <b>344</b> that represents taking adherence where the horizontal axis represents pills taken in a day and the vertical axis represents the number of days. In the illustrated graph seven <b>344</b>, the abnormalities (+/−) appear to slightly precede a period of high FSLE values.

0162Turning now to <figref idref="DRAWINGS">FIG. 45</figref>, there is illustrated a diagram <b>346</b> of an IEM ingestion matrix characterization of therapy taking according to one aspect. The vertical axis represents the number of days. The large arrow <b>348</b> pointing vertically downward represents dose adherence rate (DAR), which is the percentage (%) of doses taken within +/−1 hour from the expected time. The large arrow pointing horizontally <b>349</b> rightward represents daily taking adherence (DTA), which is the percentage (%) of days all expected doses are taken.

0163<figref idref="DRAWINGS">FIG. 46</figref> is graphical representations <b>350</b> of the FSLE risk score versus daily adherence according to one aspect. The horizontal axis represents FSLE risk score and the vertical axis represents 100-daily taking adherence (%). Four groups were plotted. The Alpha Neuropsych, Alpha Other, Hbbrim, and Hbbread.

0164<figref idref="DRAWINGS">FIG. 47</figref> is graphical representations <b>352</b> of the FSLE risk score dose daily adherence according to one aspect. The horizontal axis represents FSLE risk score and the vertical axis represents 100-dose adherence (%). Four groups were plotted. The Alpha Neuropsych, Alpha Other, Hbbrim, and Hbbread.

0165<figref idref="DRAWINGS">FIG. 48</figref> is a diagram <b>354</b> illustrating inter-week (global) variability score intrinsic dimensionality according to one aspect. Data from a data matrix <b>356</b> on the left is provided to a functional computation module <b>358</b> (e.g., computer, server, processor, engine, platform, and the like) on the right which is configured to apply principal component analysis (PCA) to the data matrix. The output of the functional computation module <b>358</b> is a whitened PCA (WPCA) data <b>360</b> that is used to generate the variation rate curve <b>362</b> |Diff(100*PCAVar/mean(PCAVar(2:end)))|. The threshold is 10% for intrinsic dimensionality.

0166<figref idref="DRAWINGS">FIG. 49</figref> is a diagram <b>364</b> for processing the WPCA data <b>360</b> to determine how many samples are above the intrinsic dimensionality <b>368</b> threshold. As shown in <figref idref="DRAWINGS">FIG. 49</figref>, the WPCA data <b>360</b> is provided to another functional computation module <b>366</b> which is configured to determine how many WPCA samples are above the threshold (10%) for intrinsic dimensionality <b>368</b>.

0167<figref idref="DRAWINGS">FIG. 50</figref> is a diagram <b>370</b> illustrating the intra-week (local) variability scores average deviation from daily pattern according to one aspect. In the illustrated diagram <b>370</b>, data from a data matrix <b>372</b> on the left is provided to a functional computation module <b>374</b> which is configured to extract weekly data (e.g., Sunday to Sunday). The output of the functional computation module <b>374</b> is WPCA data <b>376</b> plotted to represent a variation rate curve from week <b>2</b> to week <b>15</b> in the form of a differentially shaded coded matrix <b>378</b>, as shown in the illustrated example.

0168<figref idref="DRAWINGS">FIG. 51</figref> is diagram <b>380</b> for processing the WPCA data <b>376</b> into average standard deviation data according to tone aspect. As shown in <figref idref="DRAWINGS">FIG. 51</figref>, the weekly data <b>376</b> is provided to another functional computation module <b>382</b> which is configured to calculate average deviation from the daily pattern. The output of the functional computation module <b>382</b> is ASTD=Average Standard deviation <b>384</b> along days ASTD=mean(std(x) <b>384</b>, ADIFF=Average Change Rate Along Days <b>386</b> (check repeatability the same time of the day did subject) ADIFF=mean(mean(abs(diff(x)))), and Combined=[(ASTD*ADIFF)]*0.5 <b>388</b>, as shown in the illustrated example.

0169<figref idref="DRAWINGS">FIG. 52</figref> is a graphical representation <b>390</b> of global versus local [(ASTD*ADIFF)*0.5 according to one embodiment. The horizontal axis represents the global score (intrinsic dimensionality) and the vertical axis represents the local score. A differentially shaded coded vertical scale <b>391</b> to the right of the graph represents Schizophrenia, Depression, DT2, and HTN, for example.

0170<figref idref="DRAWINGS">FIG. 53</figref> is a graphical representation <b>392</b> of a data matrix (e.g., posture circadian pattern) of a first subject (Customer <b>1</b>) and <figref idref="DRAWINGS">FIG. 54</figref> is a graphical representation <b>394</b> of combined taking adherence and dose adherence according to various aspects. As shown in the illustrated example, the Global Score=3 and the Local Score=14, which is less than the threshold=17. The taking adherence=80.35% and the dose adherence=72.32%.

0171<figref idref="DRAWINGS">FIG. 55</figref> is a graphical representation <b>396</b> of a data matrix (e.g., posture circadian pattern) of a second subject (Customer <b>2</b>) and <figref idref="DRAWINGS">FIG. 56</figref> is a graphical representation <b>398</b> of combined taking adherence and dose adherence according to various aspects. As shown in the illustrated example, the Global Score=6 and the Local Score=22, which is greater than the threshold=17. The taking adherence=48.48% and the dose adherence=64.39%.

0172<figref idref="DRAWINGS">FIG. 57</figref> is a graphical representation <b>400</b> of distribution of patient scores according to one embodiment for both subjects (Customer <b>1</b> and Customer <b>2</b>) according to one aspect. The graph <b>400</b> depicts correlation between circadian score and missed dose adherence where the horizontal axis represents the local circadian score and the vertical axis represents the missed dose adherence for pill consumption behavior. A differentially shaded coded vertical scale <b>402</b> to the right of the graph represents Schizophrenia, Depression, DT2, and HTN, for example. As shown, Customer <b>1</b> has a local circadian score of 14 and Customer <b>2</b> has a local circadian score of 22.

0173<figref idref="DRAWINGS">FIG. 58</figref> is a graphical representation <b>404</b> showing the correlation between circadian score and missed dose adherence according to one aspect. The horizontal axis represents local circadian score and the vertical axis represents missed dose adherence for pill consumption behavior. A differentially shaded coded vertical scale <b>406</b> to the right of the graph represents Schizophrenia, Depression, DT2, and HTN, for example. The graph <b>400</b> shown in <figref idref="DRAWINGS">FIG. 57</figref> has been superimposed with four risk quadrants labeled low, medium, high, and medium. Thus, the patients or subjects can be classified in a particular risk class.

0174<figref idref="DRAWINGS">FIG. 59</figref> is a diagram <b>410</b> illustrating a comprehensive risk and intervention strategy according to one aspect. Once the patients are classified in a risk class <b>412</b> (low, medium, high) the patient is on-boarded <b>414</b> and a population based assessment <b>418</b> is made at node <b>416</b>. A medium touch intervention <b>420</b> is made (passive sensing application, engagement, wearable) at node <b>422</b> for medium risk class patients. Another assessment <b>424</b> based on circadian pattern is made at node <b>426</b> and a high touch intervention <b>428</b> is made (patch, pill, application) at node <b>430</b> for high risk class patients. Another assessment <b>432</b> can be made at node <b>434</b> based on circadian pattern and medication taking. A medium touch intervention <b>436</b> can be made at node <b>438</b> to medium risk class patients.

0175Accordingly, patch-derived data is well-suited as a foundation for patient risk quantification. Circadian pattern analysis using posture data is an effective component of a PICO risk score. Risk quantified at the subject group level segmented the groups into appropriate risk groupings. Individual cases illustrated the utility of patch-derived metrics to track subject temporal risk state. Additionally, therapeutic medication adherence data has potential as an additional risk input into PICO. Daily medication taking and timing adherence patterns show prospective added value through individual case analysis. Many cases where medication adherence and circadian pattern abnormalities co-exist.

0176In connection with data analysis, the system can extend posture-based risk metric procedure to other patch-derived data such as other physiological data (heart rate, temperature, acceleration) and IEM ingestion data. Risk metrics can be modified according to known issues such as when IEM ingestion is not 100% detected and fuzzy data fusion techniques can be employed to merge physiological and therapeutic data types. The therapeutic effect of individual pills (pharmacodynamics) should be accounted for since not all pills can be treated equally.

0177In connection with risk score training, the system requires subject data with outcomes, including Lifenote project data, and the inclusion of different data types into risk score such as behavioral analytics, passive sensing, contextual data, and patient data.

PICO Risk Score Distributions and Trend Analysis

0178In accordance with the techniques described herein, a joint circadian pattern (JCP) risk distribution by data set can be developed. In one aspect, four basic groupings Phase 1 Healthy group contains the lowest risk scores (as is expected). Lifenote and Helius groups comprise the middle grouping. Phase 1 Psych and Phase 2 Alpha NonPsych, Hbbread, Hbbrim group together. Alpha Psych group possesses highest risk score distribution. <figref idref="DRAWINGS">FIG. 60</figref> is a graphical representation <b>440</b> of a joint circadian pattern (JCP) risk distribution by data set where the horizontal pattern represents risk score and the vertical axis represents the cumulative distribution function for the various identified groups according to one aspect.

0179<figref idref="DRAWINGS">FIG. 61</figref> is a graphical representation <b>442</b> of a pharmacokinetic dose (PKD) risk by data set according to one aspect. The horizontal pattern represents risk score and the vertical axis represents the cumulative distribution function for the various identified groups. The PKD technique provides less obvious clustering. As shown, the Phase 1 Healthy group again has the lowest risk scores. Lifenote and Phase 1 Psych groups seem to have similar behavior above 20. Hbbread, Hbbrim are similar with Alpha groups possessing the highest risk behavior. Although Alpha NonPsych has a fatter distribution in Risk Score range of 10-50. Helius has an anomalous bump around 50—possibly due to shorter data sets.

0180<figref idref="DRAWINGS">FIG. 62</figref> is a graphical representation <b>444</b> of the overall risk distribution by data set according to one aspect. The horizontal axis represents risk score and the vertical axis represents the cumulative distribution function for the various identified groups. As before, there are four basic groupings where Phase 1 Healthy has the lowest overall risk scores, Lifenote and Helius groups cluster with the next lowest risk distribution behavior, Hbbread, Hbbrim, Alpha Non Psych, and Phase 1 Psych have the second to highest risk distributions, and Alpha Psych has the highest risk distribution.

0181In accordance with the aforementioned risk scores trend analysis techniques, the present disclosure provides a method of singling out patients for closer scrutiny and/or physician follow-up. A substantial risk score spike may be a temporary occurrence and not necessarily an indicator that a patient is in danger. Observing trends in the risk score time series provides a better indication of a patient's risk state. Other methods including methods of “Partial Trend Analysis” enable the dominant trends in subsets of the time series to be calculated based on minimizing the trend fit error. This result is parametric and may allow more or less detail into the Partial Trend by varying the maximum amount of error allowed. Thus this method can provide both the general trend (with little details) over the entire time series or a set of very short trends that closely approximate the details of the time series. An example is discussed in connection with the next three figures.

0182<figref idref="DRAWINGS">FIGS. 63-65</figref> are graphical representations <b>446</b>, <b>448</b>, <b>450</b> of partial trend analysis according to various aspects. The horizontal axis represents the day risk and the vertical axis represents the overall risk score. The graphs <b>446</b>, <b>448</b>, <b>450</b> show plots of a subject's raw PICO risk score along with three different Partial Trend approximations. The approximations were calculated using different maximum error values. Accordingly, <figref idref="DRAWINGS">FIG. 63</figref> is a graphical representation <b>446</b> of a partial trend analysis of a patient (e.g., Lifenote Subject #3) with a partial trend fit using a maximum standard error (MSE) of 171 according to one aspect. <figref idref="DRAWINGS">FIG. 64</figref> is a graphical representation <b>448</b> of a partial trend analysis of the same patient (e.g., Lifenote Subject #3) with a partial trend fit using a maximum MSE of 85. <figref idref="DRAWINGS">FIG. 65</figref> is a graphical representation <b>450</b> of a partial trend analysis of the same patient (e.g., Lifenote Subject #3) with a partial trend fit using a maximum MSE of 17. As shown in <figref idref="DRAWINGS">FIGS. 63-65</figref>, the larger the allowed error in the fit, the smoother the partial trend fit becomes. The smaller allowed errors results in a greater number of partial trends which tend to capture the details in the time series. Thus the maximum allowed error serves to fine tune the level detail that is deemed important to the analysis.

0183<figref idref="DRAWINGS">FIG. 66</figref> is a diagram <b>452</b> illustrating a partial trend analysis of a Lifenote data set according to one aspect where the horizontal axis represents the day index and the vertical axis represents the subject index. A lot of information can be gleaned by observing the partial trend slopes over a set of data. Here the MSE threshold is set at 103 (60% of maximum observed). Immediately it can be seen that subjects #1, #3 and #4 risk is trending upward toward the end of the data set. Although risk magnitude is also critical, it is not presented in this visualization.

0184<figref idref="DRAWINGS">FIG. 67</figref> is diagram <b>454</b> illustrating a partial trend analysis of a Helius data set according to one aspect where the horizontal axis represents the day index and the vertical axis represents the subject index. Over shorter data sets the partial trend heat map immediately shows which subjects increased risk over the time period. Subjects with increasing risk at present (to the data set) are 12, 13, 22, 26, and 87. However, as noted on previous slide risk magnitude is critical. Another visualization provides a combination of both magnitude and slope.

0185In the context of risk magnitude versus risk trend based on a Lifenote data set, <figref idref="DRAWINGS">FIG. 68</figref> is a graphical representation <b>456</b> of a 2-D scatter plot comparing the final risk trend magnitude and the risk trend slope over the last week of data according to one aspect. The horizontal axis represents final risk trend magnitude and the vertical axis represents weekly trend slope value. The risk trend magnitude data helps separate partial trend slope values and pinpoint which patient has higher risk values that are trending up. Subjects #3 and 4 appear to have significant risk scores that are strongly trending upwards. Plots for other data sets are not shown for conciseness and clarity of disclosure.

0186Within the context of risk magnitude versus risk trend based on a risk profile for an individual, <figref idref="DRAWINGS">FIG. 69</figref> is a graphical representation <b>458</b> of an overall risk profile for an individual according to one aspect. For an individual subject it is illuminating to observe risk magnitude vs. trend for a few given time periods. As can be seen, for one of the subjects singled out in <figref idref="DRAWINGS">FIG. 68</figref>—Lifenote subject #4—the temporal Risk profile is shown in the graph of <figref idref="DRAWINGS">FIG. 69</figref>. The risk trend slope and magnitudes are plotted for periods of 28, 14, 7, and 3 days. It can be quickly determined if the Trend Slope/Magnitude is getting better or worse.

Proteus IEM/Receiver (Patch) System

0187<figref idref="DRAWINGS">FIG. 70</figref> is conceptual illustration of a system <b>500</b> comprising an adhesive patch <b>502</b> to receive a signal from an IEM to confirm that the user <b>504</b> swallowed the medication <b>506</b> according to one aspect. Upon ingestion of the medication <b>506</b>, the patch receives a signal from the IEM and records the ingestion event. The patch <b>502</b> is configured to communicate with other communication devices such as cell phones, smart phones, access points, Wi-Fi hot spots, or any suitable wireless communication system that can connect the patch <b>502</b> with a computer processing system to perform the data analysis discussed in connection with <figref idref="DRAWINGS">FIGS. 1-69</figref>.

0188Various enabling aspects of the IEM are illustrated in <figref idref="DRAWINGS">FIGS. 71-76</figref> below. It is appreciated that the IEM may be a system which comprises a partial power source that can be activated when in contact with conductive liquid and is capable of controlling conductance to mark an event. In the instance where the system is used with the product that is ingested by the living organism, when the product that includes the system is taken or ingested, the device comes into contact with the conducting liquid of the body. When the system of the present disclosure comes into contact with the body fluid, a voltage potential is created and the system is activated. A portion of the power source is provided by the device, while another portion of the power source is provided by the conducting fluid. That is, once ingested, the system comes into contact with body liquids and the system is activated. The system uses the voltage potential difference to power up and thereafter modulates conductance to create a unique and identifiable current signature. Upon activation, the system controls the conductance and, hence, current flow to produce the current signature. In addition, various enabling aspects of the receiver/detector are illustrated in <figref idref="DRAWINGS">FIGS. 77-82</figref> below.

0189With reference to <figref idref="DRAWINGS">FIG. 71</figref>, there is shown one aspect of an ingestible device event indicator system with dissimilar metals positioned on opposite ends as system <b>2630</b>. The system <b>2630</b> can be used in association with any pharmaceutical product, as mentioned above, to determine when a patient takes the pharmaceutical product. As indicated above, the scope of the present disclosure is not limited by the environment and the product that is used with the system <b>2630</b>. For example, the system <b>2630</b> may be placed within a capsule and the capsule is placed within the conducting liquid. The capsule would then dissolve over a period of time and release the system <b>2630</b> into the conducting liquid. Thus, in one aspect, the capsule would contain the system <b>2630</b> and no product. Such a capsule may then be used in any environment where a conducting liquid is present and with any product. For example, the capsule may be dropped into a container filled with jet fuel, salt water, tomato sauce, motor oil, or any similar product. Additionally, the capsule containing the system <b>2630</b> may be ingested at the same time that any pharmaceutical product is ingested in order to record the occurrence of the event, such as when the product was taken.

0190In the specific example of the system <b>2630</b> combined with the pharmaceutical product, as the product or pill is ingested, the system <b>2630</b> is activated. The system <b>2630</b> controls conductance to produce a unique current signature that is detected, thereby signifying that the pharmaceutical product has been taken. The system <b>2630</b> includes a framework <b>2632</b>. The framework <b>2632</b> is a chassis for the system <b>2630</b> and multiple components are attached to, deposited upon, or secured to the framework <b>2632</b>. In this aspect of the system <b>2630</b>, a digestible material <b>2634</b> is physically associated with the framework <b>2632</b>. The material <b>2634</b> may be chemically deposited on, evaporated onto, secured to, or built-up on the framework all of which may be referred to herein as “deposit” with respect to the framework <b>2632</b>. The material <b>2634</b> is deposited on one side of the framework <b>2632</b>. The materials of interest that can be used as material <b>2634</b> include, but are not limited to: Cu or Cul. The material <b>2634</b> is deposited by physical vapor deposition, electrodeposition, or plasma deposition, among other protocols. The material <b>2634</b> may be from about 0.05 to about 500 μm thick, such as from about 5 to about 100 μm thick. The shape is controlled by shadow mask deposition, or photolithography and etching. Additionally, even though only one region is shown for depositing the material, each system <b>2630</b> may contain two or more electrically unique regions where the material <b>2634</b> may be deposited, as desired.

0191At a different side, which is the opposite side as shown in <figref idref="DRAWINGS">FIG. 71</figref>, another digestible material <b>2636</b> is deposited, such that materials <b>2634</b> and <b>2636</b> are dissimilar. Although not shown, the different side selected may be the side next to the side selected for the material <b>2634</b>. The scope of the present disclosure is not limited by the side selected and the term “different side” can mean any of the multiple sides that are different from the first selected side. Furthermore, even though the shape of the system is shown as a square, the shape maybe any geometrically suitable shape. Material <b>2634</b> and <b>2636</b> are selected such that they produce a voltage potential difference when the system <b>2630</b> is in contact with conducting liquid, such as body fluids. The materials of interest for material <b>2636</b> include, but are not limited to: Mg, Zn, or other electronegative metals. As indicated above with respect to the material <b>2634</b>, the material <b>2636</b> may be chemically deposited on, evaporated onto, secured to, or built-up on the framework. Also, an adhesion layer may be necessary to help the material <b>2636</b> (as well as material <b>2634</b> when needed) to adhere to the framework <b>2632</b>. Typical adhesion layers for the material <b>2636</b> are Ti, TiW, Cr or similar material. Anode material and the adhesion layer may be deposited by physical vapor deposition, electrodeposition or plasma deposition. The material <b>2636</b> may be from about 0.05 to about 500 μm thick, such as from about 5 to about 100 μm thick. However, the scope of the present disclosure is not limited by the thickness of any of the materials nor by the type of process used to deposit or secure the materials to the framework <b>2632</b>.

0192Thus, when the system <b>2630</b> is in contact with the conducting liquid, a current path, an example is shown in <figref idref="DRAWINGS">FIG. 73</figref>, is formed through the conducting liquid between material <b>2634</b> and <b>2636</b>. A control device <b>2638</b> is secured to the framework <b>2632</b> and electrically coupled to the materials <b>2634</b> and <b>2636</b>. The control device <b>2638</b> includes electronic circuitry, for example control logic that is capable of controlling and altering the conductance between the materials <b>2634</b> and <b>2636</b>.

0193The voltage potential created between the materials <b>2634</b> and <b>2636</b> provides the power for operating the system as well as produces the current flow through the conducting fluid and the system. In one aspect, the system operates in direct current mode. In an alternative aspect, the system controls the direction of the current so that the direction of current is reversed in a cyclic manner, similar to alternating current. As the system reaches the conducting fluid or the electrolyte, where the fluid or electrolyte component is provided by a physiological fluid, e.g., stomach acid, the path for current flow between the materials <b>2634</b> and <b>2636</b> is completed external to the system <b>2630</b>; the current path through the system <b>2630</b> is controlled by the control device <b>2638</b>. Completion of the current path allows for the current to flow and in turn a receiver can detect the presence of the current and recognize that the system <b>2630</b> has been activated and the desired event is occurring or has occurred.

0194In one aspect, the two materials <b>2634</b> and <b>2636</b> are similar in function to the two electrodes needed for a direct current power source, such as a battery. The conducting liquid acts as the electrolyte needed to complete the power source. The completed power source described is defined by the physical chemical reaction between the materials <b>2634</b> and <b>2636</b> of the system <b>2630</b> and the surrounding fluids of the body. The completed power source may be viewed as a power source that exploits reverse electrolysis in an ionic or a conductive solution such as gastric fluid, blood, or other bodily fluids and some tissues. Additionally, the environment may be something other than a body and the liquid may be any conducting liquid. For example, the conducting fluid may be salt water or a metallic based paint.

0195In certain aspects, these two materials are shielded from the surrounding environment by an additional layer of material. Accordingly, when the shield is dissolved and the two dissimilar materials are exposed to the target site, a voltage potential is generated.

0196Referring again to <figref idref="DRAWINGS">FIG. 71</figref>, the materials <b>2634</b> and <b>2636</b> provide the voltage potential to activate the control device <b>2638</b>. Once the control device <b>2638</b> is activated or powered up, the control device <b>2638</b> can alter conductance between the materials <b>2634</b> and <b>2636</b> in a unique manner. By altering the conductance between materials <b>2634</b> and <b>2636</b>, the control device <b>2638</b> is capable of controlling the magnitude of the current through the conducting liquid that surrounds the system <b>2630</b>. This produces a unique current signature that can be detected and measured by a receiver, which can be positioned internal or external to the body. In addition to controlling the magnitude of the current path between the materials, non-conducting materials, membrane, or “skirt” are used to increase the “length” of the current path and, hence, act to boost the conductance path, as disclosed in the U.S. patent application Ser. No. 12/238,345 filed Sep. 25, 2608, published 2609-0082645, and entitled, “In-Body Device with Virtual Dipole Signal Amplification”, the entire content of which is incorporated herein by reference. Alternatively, throughout the disclosure herein, the terms “non-conducting material,” “membrane,” and “skirt” are interchangeably with the term “current path extender” without impacting the scope or the present aspects and the claims herein. The skirt, shown in portion at <b>2635</b> and <b>2637</b>, respectively, may be associated with, e.g., secured to, the framework <b>2632</b>. Various shapes and configurations for the skirt are contemplated as within the scope of the present disclosure. For example, the system <b>2630</b> may be surrounded entirely or partially by the skirt and the skirt maybe positioned along a central axis of the system <b>2630</b> or off-center relative to a central axis. Thus, the scope of the present disclosure as claimed herein is not limited by the shape or size of the skirt. Furthermore, in other aspects, the materials <b>2634</b> and <b>2636</b> may be separated by one skirt that is positioned in any defined region between the materials <b>2634</b> and <b>2636</b>.

0197Referring now to <figref idref="DRAWINGS">FIG. 72</figref>, in another aspect of an ingestible device is shown in more detail as system <b>2640</b>. The system <b>2640</b> includes a framework <b>2642</b>. The framework <b>2642</b> is similar to the framework <b>2632</b> of <figref idref="DRAWINGS">FIG. 26</figref>. In this aspect of the system <b>2640</b>, a digestible or dissolvable material <b>2644</b> is deposited on a portion of one side of the framework <b>2642</b>. At a different portion of the same side of the framework <b>2642</b>, another digestible material <b>2646</b> is deposited, such that materials <b>2644</b> and <b>2646</b> are dissimilar. More specifically, material <b>2644</b> and <b>2646</b> are selected such that they form a voltage potential difference when in contact with a conducting liquid, such as body fluids. Thus, when the system <b>2640</b> is in contact with and/or partially in contact with the conducting liquid, then a current path, an example is shown in <figref idref="DRAWINGS">FIG. 28</figref>, is formed through the conducting liquid between material <b>2644</b> and <b>2646</b>. A control device <b>2648</b> is secured to the framework <b>2642</b> and electrically coupled to the materials <b>2644</b> and <b>2646</b>. The control device <b>2648</b> includes electronic circuitry that is capable of controlling part of the conductance path between the materials <b>2644</b> and <b>2646</b>. The materials <b>2644</b> and <b>2646</b> are separated by a non-conducting skirt <b>2649</b>. Various examples of the skirt <b>2649</b> are disclosed in U.S. Provisional Application No. 61/173,511 filed on Apr. 28, 2609 and entitled “HIGHLY RELIABLE INGESTIBLE EVENT MARKERS AND METHODS OF USING SAME” and U.S. Provisional Application No. 61/173,564 filed on Apr. 28, 2609 and entitled “INGESTIBLE EVENT MARKERS HAVING SIGNAL AMPLIFIERS THAT COMPRISE AN ACTIVE AGENT”; as well as U.S. application Ser. No. 12/238,345 filed Sep. 25, 2608, published 2609-0082645, entitled “IN-BODY DEVICE WITH VIRTUAL DIPOLE SIGNAL AMPLIFICATION”; the entire disclosure of each is incorporated herein by reference.

0198Once the control device <b>2648</b> is activated or powered up, the control device <b>2648</b> can alter conductance between the materials <b>2644</b> and <b>2646</b>. Thus, the control device <b>2648</b> is capable of controlling the magnitude of the current through the conducting liquid that surrounds the system <b>2640</b>. As indicated above with respect to system <b>2630</b>, a unique current signature that is associated with the system <b>2640</b> can be detected by a receiver to mark the activation of the system <b>2640</b>. In order to increase the “length” of the current path the size of the skirt <b>2649</b> is altered. The longer the current path, the easier it may be for the receiver to detect the current.

0199Referring now to <figref idref="DRAWINGS">FIG. 73</figref>, the system <b>2630</b> of <figref idref="DRAWINGS">FIG. 71</figref> is shown in an activated state and in contact with conducting liquid. The system <b>2630</b> is grounded through ground contact <b>2652</b>. The system <b>2630</b> also includes a sensor module <b>2674</b>, which is described in greater detail with respect to <figref idref="DRAWINGS">FIG. 72</figref>. Ion or current paths <b>2650</b> form between material <b>2634</b> to material <b>2636</b> through the conducting fluid in contact with the system <b>2630</b>. The voltage potential created between the material <b>2634</b> and <b>2636</b> is created through chemical reactions between materials <b>2634</b>/<b>2636</b> and the conducting fluid.

0200<figref idref="DRAWINGS">FIG. 74</figref> shows an exploded view of the surface of the material <b>2634</b>. The surface of the material <b>2634</b> is not planar, but rather an irregular surface <b>2654</b> as shown. The irregular surface <b>2654</b> increases the surface area of the material and, hence, the area that comes in contact with the conducting fluid.

0201In one aspect, at the surface of the material <b>2634</b>, there is chemical reaction between the material <b>2634</b> and the surrounding conducting fluid such that mass is released into the conducting fluid. The term “mass” as used herein refers to protons and neutrons that form a substance. One example includes the instant where the material is CuCl and when in contact with the conducting fluid, CuCl becomes Cu (solid) and Cl— in solution. The flow of ions into the conduction fluid is depicted by the ion paths <b>2650</b>. In a similar manner, there is a chemical reaction between the material <b>2636</b> and the surrounding conducting fluid and ions are captured by the material <b>2636</b>. The release of ions at the material <b>2634</b> and capture of ion by the material <b>2636</b> is collectively referred to as the ionic exchange. The rate of ionic exchange and, hence the ionic emission rate or flow, is controlled by the control device <b>2638</b>. The control device <b>2638</b> can increase or decrease the rate of ion flow by altering the conductance, which alters the impedance, between the materials <b>2634</b> and <b>2636</b>. Through controlling the ion exchange, the system <b>2630</b> can encode information in the ionic exchange process. Thus, the system <b>2630</b> uses ionic emission to encode information in the ionic exchange.

0202The control device <b>2638</b> can vary the duration of a fixed ionic exchange rate or current flow magnitude while keeping the rate or magnitude near constant, similar to when the frequency is modulated and the amplitude is constant. Also, the control device <b>2638</b> can vary the level of the ionic exchange rate or the magnitude of the current flow while keeping the duration near constant. Thus, using various combinations of changes in duration and altering the rate or magnitude, the control device <b>2638</b> encodes information in the current flow or the ionic exchange. For example, the control device <b>2638</b> may use, but is not limited to any of the following techniques namely, Binary Phase-Shift Keying (PSK), Frequency modulation, Amplitude modulation, on-off keying, and PSK with on-off keying.

0203As indicated above, the various aspects disclosed herein, such as systems <b>2630</b> and <b>2640</b> of <figref idref="DRAWINGS">FIGS. 71 and 72</figref>, respectively, include electronic components as part of the control device <b>2638</b> or the control device <b>2648</b>. Components that may be present include but are not limited to: logic and/or memory elements, an integrated circuit, an inductor, a resistor, and sensors for measuring various parameters. Each component may be secured to the framework and/or to another component. The components on the surface of the support may be laid out in any convenient configuration. Where two or more components are present on the surface of the solid support, interconnects may be provided.

0204As indicated above, the system, such as system <b>2630</b> and <b>2640</b>, control the conductance between the dissimilar materials and, hence, the rate of ionic exchange or the current flow. Through altering the conductance in a specific manner the system is capable of encoding information in the ionic exchange and the current signature. The ionic exchange or the current signature is used to uniquely identify the specific system. Additionally, the systems <b>2630</b> and <b>2640</b> are capable of producing various different unique exchanges or signatures and, thus, provide additional information. For example, a second current signature based on a second conductance alteration pattern may be used to provide additional information, which information may be related to the physical environment. To further illustrate, a first current signature may be a very low current state that maintains an oscillator on the chip and a second current signature may be a current state at least a factor of ten higher than the current state associated with the first current signature.

0205Referring now to <figref idref="DRAWINGS">FIG. 76</figref>, a block diagram representation of the control device <b>2638</b> is shown. The device <b>2638</b> includes a control module <b>2662</b>, a counter or clock <b>2664</b>, and a memory <b>2666</b>. Additionally, the device <b>2638</b> is shown to include a sensor module <b>2672</b> as well as the sensor module <b>2674</b>, which was referenced in <figref idref="DRAWINGS">FIG. 73</figref>. The control module <b>2662</b> has an input <b>2668</b> electrically coupled to the material <b>2634</b> and an output <b>2670</b> electrically coupled to the material <b>2636</b>. The control module <b>2662</b>, the clock <b>2664</b>, the memory <b>2666</b>, and the sensor modules <b>2672</b>/<b>2674</b> also have power inputs (some not shown). The power for each of these components is supplied by the voltage potential produced by the chemical reaction between materials <b>2634</b> and <b>2636</b> and the conducting fluid, when the system <b>2630</b> is in contact with the conducting fluid.

0206The control module <b>2662</b> controls the conductance through logic that alters the overall impedance of the system <b>2630</b>. The control module <b>2662</b> is electrically coupled to the clock <b>2664</b>. The clock <b>2664</b> provides a clock cycle to the control module <b>2662</b>. Based upon the programmed characteristics of the control module <b>2662</b>, when a set number of clock cycles have passed, the control module <b>2662</b> alters the conductance characteristics between materials <b>2634</b> and <b>2636</b>. This cycle is repeated and thereby the control device <b>2638</b> produces a unique current signature characteristic. The control module <b>2662</b> is also electrically coupled to the memory <b>2666</b>. Both the clock <b>2664</b> and the memory <b>2666</b> are powered by the voltage potential created between the materials <b>2634</b> and <b>2636</b>.

0207The control module <b>2662</b> is also electrically coupled to and in communication with the sensor modules <b>2672</b> and <b>2674</b>. In the aspect shown, the sensor module <b>2672</b> is part of the control device <b>2638</b> and the sensor module <b>2674</b> is a separate component. In alternative aspects, either one of the sensor modules <b>2672</b> or <b>2674</b> can be used without the other, and the scope of the present disclosure is not limited by the structural or functional location of the sensor modules <b>2672</b> or <b>2674</b>. Additionally, any component of the system <b>2630</b> may be functionally or structurally moved, combined, or repositioned without limiting the scope of the present disclosure as claimed. Thus, it is possible to have one single structure, for example a processor, which is designed to perform the functions of all of the following modules: the control module <b>2662</b>, the clock <b>2664</b>, the memory <b>2666</b>, and the sensor module <b>2672</b> or <b>2674</b>. On the other hand, it is also within the scope of the present disclosure to have each of these functional components located in independent structures that are linked electrically and able to communicate.

0208Referring again to <figref idref="DRAWINGS">FIG. 76</figref>, the sensor modules <b>2672</b> or <b>2674</b> can include any of the following sensors: temperature, pressure, pH level, and conductivity. In one aspect, the sensor modules <b>2672</b> or <b>2674</b> gather information from the environment and communicate the analog information to the control module <b>2662</b>. The control module then converts the analog information to digital information and the digital information is encoded in the current flow or the rate of the transfer of mass that produces the ionic flow. In another aspect, the sensor modules <b>2672</b> or <b>2674</b> gather information from the environment and convert the analog information to digital information and then communicate the digital information to control module <b>2662</b>. In the aspect shown in <figref idref="DRAWINGS">FIG. 73</figref>, the sensor modules <b>2674</b> is shown as being electrically coupled to the material <b>2634</b> and <b>2636</b> as well as the control device <b>2638</b>. In another aspect, as shown in <figref idref="DRAWINGS">FIG. 76</figref>, the sensor module <b>2674</b> is electrically coupled to the control device <b>2638</b> at a connection. The connection acts as both a source for power supply to the sensor module <b>2674</b> and a communication channel between the sensor module <b>2674</b> and the control device <b>2638</b>.

0209Referring now to <figref idref="DRAWINGS">FIG. 75</figref>, the system <b>2630</b> includes a pH sensor module <b>2676</b> connected to a material <b>2639</b>, which is selected in accordance with the specific type of sensing function being performed. The pH sensor module <b>2676</b> is also connected to the control device <b>2638</b>. The material <b>2639</b> is electrically isolated from the material <b>2634</b> by a non-conductive barrier <b>2655</b>. In one aspect, the material <b>2639</b> is platinum. In operation, the pH sensor module <b>2676</b> uses the voltage potential difference between the materials <b>2634</b>/<b>2636</b>. The pH sensor module <b>2676</b> measures the voltage potential difference between the material <b>2634</b> and the material <b>2639</b> and records that value for later comparison. The pH sensor module <b>2676</b> also measures the voltage potential difference between the material <b>2639</b> and the material <b>2636</b> and records that value for later comparison. The pH sensor module <b>2676</b> calculates the pH level of the surrounding environment using the voltage potential values. The pH sensor module <b>2676</b> provides that information to the control device <b>2638</b>. The control device <b>2638</b> varies the rate of the transfer of mass that produces the ionic transfer and the current flow to encode the information relevant to the pH level in the ionic transfer, which can be detected by a receiver. Thus, the system <b>2630</b> can determine and provide the information related to the pH level to a source external to the environment.

0210As indicated above, the control device <b>2638</b> can be programmed in advance to output a pre-defined current signature. In another aspect, the system can include a receiver system that can receive programming information when the system is activated. In another aspect, not shown, the switch <b>2664</b> and the memory <b>2666</b> can be combined into one device.

0211In addition to the above components, the system <b>2630</b> may also include one or other electronic components. Electrical components of interest include, but are not limited to: additional logic and/or memory elements, e.g., in the form of an integrated circuit; a power regulation device, e.g., battery, fuel cell or capacitor; a sensor, a stimulator, etc.; a signal transmission element, e.g., in the form of an antenna, electrode, coil, etc.; a passive element, e.g., an inductor, resistor, etc.

0212<figref idref="DRAWINGS">FIG. 77</figref> provides a functional block diagram of how a receiver may implement a coherent demodulation protocol, according to one aspect of the disclosure. It should be noted that only a portion of the receiver is shown in <figref idref="DRAWINGS">FIG. 77</figref>. <figref idref="DRAWINGS">FIG. 77</figref> illustrates the process of mixing the signal down to baseband once the carrier frequency (and carrier signal mixed down to carrier offset) is determined. A carrier signal <b>3221</b> is mixed with a second carrier signal <b>3222</b> at mixer <b>3223</b>. A narrow low-pass filter <b>3220</b> is applied of appropriate bandwidth to reduce the effect of out-of-bound noise. Demodulation occurs at functional blocks <b>3225</b> in accordance with the coherent demodulation scheme of the present disclosure. The unwrapped phase <b>3230</b> of the complex signal is determined. An optional third mixer stage, in which the phase evolution is used to estimate the frequency differential between the calculated and real carrier frequency can be applied. The structure of the packet is then leveraged to determine the beginning of the coding region of the BPSK signal at block <b>3240</b>. Mainly, the presence of the sync header, which appears as an FM porch in the amplitude signal of the complex demodulated signal is used to determine the starting bounds of the packet. Once the starting point of the packet is determined the signal is rotated at block <b>3250</b> on the IQ plane and standard bit identification and eventually decoded at block <b>3260</b>.

0213In addition to demodulation, the transbody communication module may include a forward error correction module, which module provides additional gain to combat interference from other unwanted signals and noise. Forward error correction functional modules of interest include those described in PCT Application Serial No. PCT/US2007/024325 published as WO/2008/063626; the disclosure of which is herein incorporated by reference. In some instances, the forward error correction module may employ any convenient protocol, such as Reed-Solomon, Golay, Hamming, BCH, and Turbo protocols to identify and correct (within bounds) decoding errors.

0214Receivers of the disclosure may further employ a beacon functionality module. In various aspects, the beacon switching module may employ one or more of the following: a beacon wakeup module, a beacon signal module, a wave/frequency module, a multiple frequency module, and a modulated signal module.

0215The beacon switching module may be associated with beacon communications, e.g., a beacon communication channel, a beacon protocol, etc. For the purpose of the present disclosure, beacons are typically signals sent either as part of a message or to augment a message (sometimes referred to herein as “beacon signals”). The beacons may have well-defined characteristics, such as frequency. Beacons may be detected readily in noisy environments and may be used for a trigger to a sniff circuit, such as described below.

0216In one aspect, the beacon switching module may comprise the beacon wakeup module, having wakeup functionality. Wakeup functionality generally comprises the functionality to operate in high power modes only during specific times, e.g., short periods for specific purposes, to receive a signal, etc. An important consideration on a receiver portion of a system is that it be of low power. This feature may be advantageous in an implanted receiver, to provide for both small size and to preserve a long-functioning electrical supply from a battery. The beacon switching module enables these advantages by having the receiver operate in a high power mode for very limited periods of time. Short duty cycles of this kind can provide optimal system size and energy draw features.

0217In practice, the receiver may “wake up” periodically, and at low energy consumption, to perform a “sniff function” via, for example, a sniff circuit. For the purpose of the present application, the term “sniff function” generally refers to a short, low-power function to determine if a transmitter is present. If a transmitter signal is detected by the sniff function, the device may transition to a higher power communication decode mode. If a transmitter signal is not present, the receiver may return, e.g., immediately return, to sleep mode. In this manner, energy is conserved during relatively long periods when a transmitter signal is not present, while high-power capabilities remain available for efficient decode mode operations during the relatively few periods when a transmit signal is present. Several modes, and combination thereof, may be available for operating the sniff circuit. By matching the needs of a particular system to the sniff circuit configuration, an optimized system may be achieved.

0218Another view of a beacon module is provided in the functional block diagram shown in <figref idref="DRAWINGS">FIG. 78</figref>. The scheme outlined in <figref idref="DRAWINGS">FIG. 78</figref> outlines one technique for identifying a valid beacon. The incoming signal <b>3360</b> represents the signals received by electrodes, bandpass filtered (such as from 10 KHz to 34 KHz) by a high frequency signaling chain (which encompasses the carrier frequency), and converted from analog to digital. The signal <b>3360</b> is then decimated at block <b>3361</b> and mixed at the nominal drive frequency (such as, 12.5 KHz, 20 KHz, etc.) at mixer <b>3362</b>. The resulting signal is decimated at block <b>3364</b> and low-pass filtered (such as 5 KHz BW) at block <b>3365</b> to produce the carrier signal mixed down to carrier offset-signal <b>3369</b>. Signal <b>3369</b> is further processed by blocks <b>3367</b> (fast Fourier transform and then detection of two strongest peaks) to provide the true carrier frequency signal <b>3368</b>. This protocol allows for accurate determination of the carrier frequency of the transmitted beacon.

0219<figref idref="DRAWINGS">FIG. 79</figref> provides a block functional diagram of an integrated circuit component of a signal receiver according to an aspect of the disclosure. In <figref idref="DRAWINGS">FIG. 79</figref>, a receiver <b>3700</b> includes electrode input <b>3710</b>. Electrically coupled to the electrode input <b>3710</b> are transbody conductive communication module <b>3720</b> and physiological sensing module <b>3730</b>. In one aspect, transbody conductive communication module <b>3720</b> is implemented as a high frequency (HF) signal chain and physiological sensing module <b>3730</b> is implemented as a low frequency (LF) signal chain. Also shown are CMOS temperature sensing module <b>3740</b> (for detecting ambient temperature) and a 3-axis accelerometer <b>3750</b>. Receiver <b>3700</b> also includes a processing engine <b>3760</b> (for example, a microcontroller and digital signal processor), non-volatile memory <b>3770</b> (for data storage) and wireless communication module <b>3780</b> (for data transmission to another device, for example in a data upload action).

0220<figref idref="DRAWINGS">FIG. 80</figref> provides a more detailed block diagram of a circuit configured to implement the block functional diagram of the receiver depicted in <figref idref="DRAWINGS">FIG. 79</figref>, according to one aspect of the disclosure. In <figref idref="DRAWINGS">FIG. 80</figref>, a receiver <b>3800</b> includes electrodes e<b>1</b>, e<b>2</b> and e<b>3</b> (<b>3811</b>, <b>3812</b> and <b>3813</b>) which, for example, receive the conductively transmitted signals by an IEM and/or sense physiological parameters or biomarkers of interest. The signals received by the electrodes <b>3811</b>, <b>3812</b>, and <b>3813</b> are multiplexed by multiplexer <b>3820</b> which is electrically coupled to the electrodes.

0221Multiplexer <b>3820</b> is electrically coupled to both high band pass filter <b>3830</b> and low band pass filter <b>3840</b>. The high and low frequency signal chains provide for programmable gain to cover the desired level or range. In this specific aspect, high band pass filter <b>3830</b> passes frequencies in the 10 KHz to 34 KHz band while filtering out noise from out-of-band frequencies. This high frequency band may vary, and may include, for example, a range of 3 KHz to 300 KHz. The passing frequencies are then amplified by amplifier <b>3832</b> before being converted into a digital signal by converter <b>3834</b> for input into high power processor <b>3880</b> (shown as a DSP) which is electrically coupled to the high frequency signal chain.

0222Low band pass filter <b>3840</b> is shown passing lower frequencies in the range of 0.5 Hz to 150 Hz while filtering out out-of-band frequencies. The frequency band may vary, and may include, for example, frequencies less than 300 Hz, such as less than 200 Hz, including less than 150 Hz. The passing frequency signals are amplified by amplifier <b>3842</b>. Also shown is accelerometer <b>3850</b> electrically coupled to second multiplexer <b>3860</b>. Multiplexer <b>3860</b> multiplexes the signals from the accelerometer with the amplified signals from amplifier <b>3842</b>. The multiplexed signals are then converted to digital signals by converter <b>3864</b> which is also electrically coupled to low power processor <b>3870</b>.

0223In one aspect, a digital accelerometer (such as one manufactured by Analog Devices), may be implemented in place of accelerometer <b>3850</b>. Various advantages may be achieved by using a digital accelerometer. For example, because the signals the digital accelerometer would produce signals already in digital format, the digital accelerometer could bypass converter <b>3864</b> and electrically couple to the low power microcontroller <b>3870</b>—in which case multiplexer <b>3860</b> would no longer be required. Also, the digital signal may be configured to turn itself on when detecting motion, further conserving power. In addition, continuous step counting may be implemented. The digital accelerometer may include a FIFO buffer to help control the flow of data sent to the low power processor <b>3870</b>. For instance, data may be buffered in the FIFO until full, at which time the processor may be triggered to turn awaken from an idle state and receive the data.

0224Low power processor <b>3870</b> may be, for example, an MSP430 microcontroller from Texas Instruments. Low power processor <b>3870</b> of receiver <b>3800</b> maintains the idle state, which as stated earlier, requires minimal current draw—e.g., 10 μA or less, or 1 μA or less.

0225High power processor <b>3880</b> may be, for example, a VC5509 digital signal process from Texas Instruments. The high power processor <b>3880</b> performs the signal processing actions during the active state. These actions, as stated earlier, require larger amounts of current than the idle state—e.g., currents of 30 μA or more, such as 50 μA or more—and may include, for example, actions such as scanning for conductively transmitted signals, processing conductively transmitted signals when received, obtaining and/or processing physiological data, etc.

0226The receiver may include a hardware accelerator module to process data signals. The hardware accelerator module may be implemented instead of, for example, a DSP. Being a more specialized computation unit, it performs aspects of the signal processing algorithm with fewer transistors (less cost and power) compared to the more general purpose DSP. The blocks of hardware may be used to “accelerate” the performance of important specific function(s). Some architectures for hardware accelerators may be “programmable” via microcode or VLIW assembly. In the course of use, their functions may be accessed by calls to function libraries.

0227The hardware accelerator (HWA) module comprises an HWA input block to receive an input signal that is to be processed and instructions for processing the input signal; and, an HWA processing block to process the input signal according to the received instructions and to generate a resulting output signal. The resulting output signal may be transmitted as needed by an HWA output block.

0228Also shown in <figref idref="DRAWINGS">FIG. 80</figref> is flash memory <b>3890</b> electrically coupled to high power processor <b>3880</b>. In one aspect, flash memory <b>3890</b> may be electrically coupled to low power processor <b>3870</b>, which may provide for better power efficiency.

0229Wireless communication element <b>3895</b> is shown electrically coupled to high power processor <b>3880</b> and may include, for example, a BLUETOOTH™ wireless communication transceiver. In one aspect, wireless communication element <b>3895</b> is electrically coupled to high power processor <b>3880</b>. In another aspect, wireless communication element <b>3895</b> is electrically coupled to high power processor <b>3880</b> and low power processor <b>3870</b>. Furthermore, wireless communication element <b>3895</b> may be implemented to have its own power supply so that it may be turned on and off independently from other components of the receiver—e.g., by a microprocessor.

0230<figref idref="DRAWINGS">FIG. 81</figref> provides a view of a block diagram of hardware in a receiver according to an aspect of the disclosure related to the high frequency signal chain. In <figref idref="DRAWINGS">FIG. 81</figref>, receiver <b>3900</b> includes receiver probes (for example in the form of electrodes <b>3911</b>, <b>3912</b> and <b>3913</b>) electrically coupled to multiplexer <b>3920</b>. Also shown are high pass filter <b>3930</b> and low pass filter <b>3940</b> to provide for a band pass filter which eliminates any out-of-band frequencies. In the aspect shown, a band pass of 10 KHz to 34 KHz is provided to pass carrier signals falling within the frequency band. Example carrier frequencies may include, but are not limited to, 12.5 KHz and 20 KHz. One or more carriers may be present. In addition, the receiver <b>3900</b> includes analog to digital converter <b>3950</b>—for example, sampling at 500 KHz. The digital signal can thereafter be processed by the DSP. Shown in this aspect is DMA to DSP unit <b>3960</b> which sends the digital signal to dedicated memory for the DSP. The direct memory access provides the benefit of allowing the rest of the DSP to remain in a low power mode.

0231As stated earlier, for each receiver state, the high power functional block may be cycled between active and inactive states accordingly. Also, for each receiver state, various receiver elements (such as circuit blocks, power domains within processor, etc.) of a receiver may be configured to independently cycle from on and off by the power supply module. Therefore, the receiver may have different configurations for each state to achieve power efficiency.

0232An example of a system of the disclosure is shown in <figref idref="DRAWINGS">FIG. 82</figref>. In <figref idref="DRAWINGS">FIG. 82</figref>, system <b>4000</b> includes a pharmaceutical composition <b>4010</b> that comprises an IEM. Also present in the system <b>4000</b> is signal receiver <b>4020</b>. Signal receiver <b>4020</b> is configured to detect a signal emitted from the identifier of the IEM <b>4010</b>. Signal receiver <b>4020</b> also includes physiologic sensing capability, such as ECG and movement sensing capability. Signal receiver <b>4020</b> is configured to transmit data to a patient's an external device or PDA <b>4030</b> (such as a smart phone or other wireless communication enabled device), which in turn transmits the data to a server <b>4040</b>. Server <b>4040</b> may be configured as desired, e.g., to provide for patient directed permissions. For example, server <b>4040</b> may be configured to allow a family caregiver <b>4050</b> to participate in the patient's therapeutic regimen, e.g., via an interface (such as a web interface) that allows the family caregiver <b>4050</b> to monitor alerts and trends generated by the server <b>4040</b>, and provide support back to the patient, as indicated by arrow <b>4060</b>. The server <b>4040</b> may also be configured to provide responses directly to the patient, e.g., in the form of patient alerts, patient incentives, etc., as indicated by arrow <b>4065</b> which are relayed to the patient via PDA <b>4030</b>. Server <b>4040</b> may also interact with a health care professional (e.g., RN, physician) <b>4055</b>, which can use data processing algorithms to obtain measures of patient health and compliance, e.g., wellness index summaries, alerts, cross-patient benchmarks, etc., and provide informed clinical communication and support back to the patient, as indicated by arrow <b>4080</b>.

0233<figref idref="DRAWINGS">FIG. 83</figref> illustrates one aspect of a personal communication system <b>1000</b> for obtaining patient physiological and IEM system information into a computer system according to one aspect. As illustrated in <figref idref="DRAWINGS">FIG. 83</figref>, a receiver, otherwise referred to herein as a body-associated personal communicator <b>1004</b> or patch, is positioned on a living subject <b>1002</b>. The living subject <b>1002</b> may be a human or non-human being. In various aspects, the body-associated personal communicator <b>1004</b> may be realized in many forms and configurations including sensor-enabled patches, watches, and jewelry, as shown in <figref idref="DRAWINGS">FIG. 83</figref>, for example, as well as a bandage with an adhesive portion, wristbands, earrings, bracelets, rings, pendants, clothing, undergarments, hats, caps, scarves, pins, accessories, belts, shoes, eyeglasses, contact lenses, hearing-aides, subcutaneous implants, and other devices that are wearable, implantable, or semi-implantable on or in the living subject <b>1002</b> without limitation. The body-associated personal communicator <b>1004</b> is configured to communicate with the living subject <b>1002</b> and an external local node <b>1006</b>. The external local node <b>1006</b> is configured to communicate with a remote node <b>1010</b> via a network <b>1008</b>. In one aspect, the body-associated personal communicator <b>1004</b> is configured to communicate with the remote node <b>1010</b> directly. It will be appreciated that in the context of the present disclosure, communication is intended to encompass communications to and from the personal communicator <b>1004</b> and the external local node <b>1006</b>. Likewise, communication is intended to encompass communications to and from the body-associated personal communicator <b>1004</b> and the remote node <b>1010</b> as well as communications to and from the external local node <b>1006</b> and the remote node <b>1010</b>.

0234The body-associated personal communicator <b>1004</b> may comprise any number of distinct physiologic parameter or biomarker collecting and/or sensing capabilities. The number of distinct parameters or biomarker collecting and/or sensing capabilities may vary e.g., one or more, two or more, three or more, four or more, five or more, ten or more, and so on. In certain configurations, the body-associated personal communicator <b>1004</b> comprises one or more active components that are able to dynamically monitor and record individual physiologic parameters and/or biomarkers associated with the living subject <b>1002</b>. Such components include, without limitation, sensors, electronic recording devices, processors, memory, communication components. In one aspect, the body-associated personal communicator <b>1004</b> may include an on-board battery to supply electrical power to the active components. The physiologic parameter or biomarker sensing abilities may include sensing cardio-data, including heart rate, (ECG, and the like, respiration rate, temperature, pressure, chemical composition of fluid, e.g., analyte in blood, fluid state, blood flow rate, physical activity, sleep, accelerometer motion data, without limitation, for example.

0235In one aspect, the body-associated personal communicator <b>1004</b> provides specific information about the physiologic state of the subject <b>1002</b>. In another aspect, some of this information may be derived from sensors embedded in the body-associated personal communicator <b>1004</b>. The subject <b>1002</b> may obtain the body-associated personal communicator <b>1004</b> with a prescription, for example, and then wear the body-associated personal communicator <b>1004</b> for a prescribed period, e.g., hours, days, weeks, months, years.

0236In one aspect, the body-associated personal communicator <b>1004</b> includes, is configured to (a) monitor and record individual physiology, e.g., physical activity, heart rate, respiration, temperature, sleep, etc., of the living subject <b>1002</b> and (b) communicate these parameters beyond the body of the living subject <b>1002</b> to other client devices, e.g., mobile phones, computers, internet servers, etc., in order to (c) enable support and collaboration for fitness, wellbeing, disease management, sport, entertainment, gaming, and other applications on a social media platform. A challenge for such body-associated personal communicators <b>104</b> is creating a compelling rationale for the individual <b>1002</b> to wear or use the body-associated personal communicator <b>1004</b> on a continuous basis—for example, to apply an adhesive bandage-based body-associated personal communicator <b>1004</b> to their skin for weeks, months and potentially years and accept the possibility of its inconveniences and limitations, such as (i) potential skin irritation, (ii) the burden of frequent application and removal, and (iii) a feeling of intrusiveness into the wearer's daily life. An opportunity for the personal communicator <b>1004</b> is to exploit fundamental “intimacy” advantages they have over other sensor-enabled and communication devices that are not worn on or in the body—a body-associated personal communicator <b>1004</b> interface with the individual <b>1002</b> is by definition highly personal and tangible, with the ability to have private, communication between the individual and the personal communicator (leveraging physical, tactile “body language” or other signals), where the communication is substantially undetectable by others. In this manner, the body-associated personal communicator <b>1004</b> may enable product and service possibilities not feasible with other approaches. The body language opportunity seeks to overcome at least some of the challenges and burdens of the body-associated personal communicator <b>1004</b> to create a compelling rationale to make the body-associated personal communicator <b>1004</b> as indispensable to a consumer as the mobile phone as an extension of their mind and body. In one aspect, discreet communications between the body-associated personal communicator <b>1004</b> and the living subject <b>1002</b> can be auditory via a small earpiece placed inside the ear canal, or visual via images projected on specialized eye glasses worn by living subject <b>1002</b>. In other aspects, discreet modes of communication between the living subject <b>1002</b> and the personal communicator <b>1004</b> include, without limitation, visual, auditory, vibratory, tactile, olfactory, and taste as described in the form of illustrative examples hereinbelow.

0237In one aspect, the body-associated personal communicator <b>1004</b>, for example a sensor patch that adheres to the skin of an individual such as the living subject <b>1002</b>, communicates with its wearer by sending and receiving tactile or other signals. The default settings may be modified such that the body-associated personal communicator <b>1004</b> discreetly vibrates or pulses in a specific manner or pattern, e.g., time or space based, to remind the subject <b>1002</b> of important events or to communicate important personalized messages to the wearer. The default settings also may be modified such that the subject <b>1002</b> can transmit and record meaningful inputs and messages to the body-associated personal communicator <b>1004</b> by communicating a simple language of finger taps, jiggles, scratches or other physical inputs initiated by the subject <b>1002</b>. Through the body-associated personal communicator <b>1004</b> communications architecture, e.g., a BLUETOOTH™ or other communication links to other devices beyond the body, the composite set of sensed physiology, tactile inputs, and outputs can be transmitted to other individuals, groups, caregivers, and related products, e.g., online games, of the subject's <b>1002</b> choosing via the external local node <b>1006</b>, network <b>1008</b>, and/or the remote node <b>1010</b>. The features of the body-associated personal communicator <b>1004</b> are based on a sustained behavior change mechanism and it increases the value and potential of body-associated personal communicators <b>1004</b> and the likelihood that consumers will seek out, use, and benefit from such body-associated personal communicators <b>1004</b>.

0238In-body communications include any communication of data or information via the body of the living subject <b>1002</b>, i.e., communication via or associated with inter-body aspects, intra-body aspects, and a combination of the same. For example, inter-body aspects include communications associated with devices designed to attach to a body surface. Intra-body aspects include communications associated with data generated from within the body, e.g., by the body itself or by a device implanted, ingested, or otherwise locatable in, or partially in, the body. For example, intra-body communications are disclosed in the U.S. Provisional Patent Application No. 61/251,088, the entire content of which is hereby incorporated by reference.

0239Communications include and/or may be associated with software, hardware, circuitry, various devices, and combinations thereof.

0240The devices include devices associated with physiologic data generation, transmission, reception, communication. The devices further include various implantable, ingestible, insertable, and/or attachable devices associated with the human body or other living organisms. The devices still further include multimedia devices such as telephones, stereos, audio players, PDAs, handheld devices, and multimedia players.

0241The system for incorporating physiologic data enables exchange, transmission, receipt, manipulation, management, storage, and other activities and events related to physiologic data. Such activities and events may be contained within the system for incorporating physiologic data, partially integrated with the system for incorporating physiologic data, or associated with externalities, e.g., activities, systems, components, and the like which are external to the system for incorporating physiologic data.

0242The physiologic data environment includes any source of information or data, including remote computer systems, local computer devices. The information or data may comprise physiologic data in whole or in part, e.g., aggregated or generated with other types of data. The physiologic data may be pure or refined, e.g., physiologic data from which inferences are drawn.

0243As shown in <figref idref="DRAWINGS">FIG. 83</figref>, the body-associated personal communicator <b>1004</b>, regardless of form factor or implementation is in communication with an external local node <b>1006</b>. In one aspect, the body-associated personal communicator <b>1004</b> includes the capability of communicating, e.g., receiving, transmitting, generating, and recording data directly or indirectly from the living subject <b>1002</b>. Although the data may include physiologic data, it is not limited as such. Any data of a physiologic nature may be associated with the living subject <b>1002</b>. The physiologic data may include, for example, heart rate, heart rate variability, respiration rate, body temperature, temperature of local environment, three-axis measurement of activity and torso angle, as well as other physiologic data, metrics, inertial measurements comprising at least an accelerometer, a gyroscope, and a magnetometer, and indicators associated with one or more individuals. The physiologic data may be communicated at various times or time intervals to the external local node <b>1006</b>. For example, the communication may be real-time, i.e., in close temporal proximity to a time in which the physiologic data were generated, measured, ascertained, or on an historical basis, i.e., in far temporal proximity to a time in which the physiologic data was generated, measured, ascertained. In various aspects, the physiologic data may be associated with a variety of devices, e.g., cardiac device.

0244In one aspect, the external local node <b>1006</b> may be configured as a communication hub and may include any hardware device, software, and/or communications component(s), as well as systems, subsystems, and combinations of the same which generally function to communicate physiologic and non-physiologic data between the personal communicator <b>1004</b> and the external local node <b>1006</b>. Communication of the data includes receiving, storing, manipulating, displaying, processing, and/or transmitting the data to the remote node <b>1010</b> via the network <b>1008</b>.

0245In various aspects, the external local node <b>1006</b> also functions to communicate, e.g., receive and transmit, non-physiologic data. Example of non-physiologic data include gaming rules and data generated by a separate cardiac-related device such as an implanted pacemaker and communicated to the hub directly or indirectly, e.g., via the personal communicator <b>104</b>.

0246Broad categories of external local nodes <b>1006</b> include, for example, base stations, personal communication devices, handheld devices, and mobile telephones. In various aspects, the external local node <b>1006</b> may be implemented as a handheld portable device, computer, mobile telephone, sometimes referred to as a smartphone, tablet personal computer (PC), kiosk, desktop computer, laptop computer, game console, or any combination thereof. Although some aspects of the external local node <b>1006</b> may be described with a mobile or fixed computing device implemented as a smart phone, personal digital assistant, laptop, desktop computer by way of example, it may be appreciated that the various aspects are not limited in this context. For example, a mobile computing device may comprise, or be implemented as, any type of wireless device, mobile station, or portable computing device with a self-contained power source, e.g., battery, such as the laptop computer, ultra-laptop computer, personal digital assistant (PDA), cellular telephone, combination cellular telephone/PDA, mobile unit, subscriber station, user terminal, portable computer, handheld computer, palmtop computer, wearable computer, media player, pager, messaging device, data communication device, and so forth. A fixed computing device, for example, may be implemented as a desk top computer, workstation, client/server computer, and so forth.

0247The external local node <b>1006</b> comprises personal communication devices including, for example, devices having communication and computer functionality and typically intended for individual use, e.g., mobile computers, sometimes referred to as “handheld devices.” Base stations comprise any device or appliance capable of receiving data such as physiologic data. Examples include computers, such as desktop computers and laptop computers, and intelligent devices/appliances. Intelligent devices/appliances include consumer and home devices and appliances that are capable of receipt of data such as physiologic data. Intelligent devices/appliances may also perform other data-related functions, e.g., transmit, display, store, and/or process data. Examples of intelligent devices/appliances include refrigerators, weight scales, toilets, televisions, door frame activity monitors, bedside monitors, bed scales. Such devices and appliances may include additional functionality such as sensing or monitoring various physiologic data, e.g., weight, heart rate. Mobile telephones include telephonic communication devices associated with various mobile technologies, e.g., cellular networks.

0248In various aspects, the external local node <b>1006</b> may provide voice and/or data communications functionality in accordance with different types of cellular radiotelephone systems. Examples of cellular radiotelephone systems may include Code Division Multiple Access (CDMA) systems, Global System for Mobile Communications (GSM) systems, North American Digital Cellular (NADC) systems, Time Division Multiple Access (TDMA) systems, Extended-TDMA (E-TDMA) systems, Narrowband Advanced Mobile Phone Service (NAMPS) systems, 3G systems such as Wide-band CDMA (WCDMA), CDMA-2000, Universal Mobile Telephone System (UMTS) systems, WiMAX (Worldwide Interoperability for Microwave Access, LTE (Long Term Evolution) and so forth.

0249In various embodiments, the external local node <b>1006</b> may be configured to provide voice and/or data communications functionality in accordance with different types of wireless network systems or protocols. Examples of suitable wireless network systems offering data communication services may include the Institute of Electrical and Electronics Engineers (IEEE) 802.xx series of protocols, such as the IEEE 802.1a/b/g/n series of standard protocols and variants (also referred to as “WiFi”), the IEEE 802.16 series of standard protocols and variants (also referred to as “WiMAX”), the IEEE 802.20 series of standard protocols and variants, and so forth. A mobile computing device may also utilize different types of shorter range wireless systems, such as a Bluetooth system operating in accordance with the Bluetooth Special Interest Group (SIG) series of protocols, including Bluetooth Specification versions v1.0, v1.1, v1.2, v1.0, v2.0 with Enhanced Data Rate (EDR), as well as one or more Bluetooth Profiles, and so forth. Other examples may include systems using infrared techniques or near-field communication techniques and protocols, such as electromagnetic induction (EMI) techniques.

0250In one aspect, the external local node <b>1006</b>, for example, the hub, includes a software application associated with a mobile telephone of a patient. The application and mobile telephone function to receive physiologic data from a receiver, which, in turn, receives the physiologic data directly from an individual or indirectly, e.g., via a device. Examples of devices include cardiac devices and ingestible devices. The hub stores, manipulates, and/or forwards the data, alone or in combination with other data, via the network <b>1008</b> to a remote node <b>1010</b>.

0251In various aspects, the external local node <b>1006</b> (hub) receives, generates, communicates, and/or transmits, physiologic data, alone or in combination with other data, i.e., non-physiologic data such as ingestion information from IEMs or various sources. Communication from the external local node <b>106</b> includes any transmission means or carriers, and combinations thereof, including wireless, wired, RF, conductive, etc. as is known in the art or as may become available in the future.

0252In various aspects, the handheld device includes software, e.g., a software agent/application, associated with the physiologic data. In various aspects of the handheld device, the software is preconfigured, i.e., configurable by the manufacturer/retailer; configurable by the consumer, i.e., downloadable from a website; or a combination of the same.

0253The base station includes systems, subsystems, devices, and/or components that receive, transmit, and/or relay the physiologic data. In various aspects, the base station communicably interoperates with a receiver such as the body-associated personal communicator <b>1004</b> and a communications network <b>1008</b> such as the Internet. Examples of base stations are computers, e.g., servers, personal computers, desktop computers, laptop computers, intelligent devices/appliances, etc., as heretofore discussed.

0254In various aspects, the base station may be embodied as an integrated unit or as distributed components, e.g., a desktop computer and a mobile telephone in communication with one another and in communication with a patch receiver and the Internet.

0255In various aspects, the base station includes the functionality to wirelessly receive and/or wirelessly transmit data, e.g., physiologic data received from and transmitted to the body-associated personal communicator <b>1004</b> and the Internet.

0256Further, in various aspects, the base station may incorporate and/or be associated with, e.g., communicate with, various devices. Such devices may generate, receive, and/or communicate data, e.g., physiologic data. The devices include, for example, “intelligent” devices such as gaming devices, e.g., electronic slot machines, handheld electronic games, electronic components associated with games and recreational activities.

0257The mobile telephone includes, for example, devices such as a short-range, portable electronic device used for mobile voice or data communication over a network of specialized cell site base stations. The mobile telephone is sometimes known as or referred to as “mobile,” “wireless,” “cellular phone,” “cell phone,” or “hand phone (HP).”

0258In addition to the standard voice function of a telephone, various aspects of mobile telephones may support many additional services and accessories such as short message service (SMS) for text messaging, email, packet switching for access to the Internet, Java gaming, wireless, e.g., short range data/voice communications, infrared, camera with video recorder, and multimedia messaging system (MMS) for sending and receiving photos and video. Some aspects of mobile telephones connect to a cellular network of base stations (cell sites), which is, in turn, interconnected to the public switched telephone network (PSTN) or satellite communications in the case of satellite phones. Various aspects of mobile telephones can connect to the Internet, at least a portion of which can be navigated using the mobile telephones.

0259In various aspects, the mobile telephone includes software, e.g., a software agent/application, associated with the physiologic data. One example is an auto refill application related to or integrated with an auto refill system to facilitate automated prescription refill functions. In various aspects of the mobile telephone, the software is preconfigured, i.e., configurable by the manufacturer/retailer; configurable by the consumer, i.e., downloadable from a website; or a combination of the same.

0260Further, various aspects of the hub include combinations of devices. One such combination is the body-associated personal communicator <b>1004</b> in communication with the handheld device or the mobile telephone. Thus, for example, the body-associated personal communicator <b>1004</b> wirelessly transmits physiologic data to the mobile telephone having a receiver and a software agent available thereon. The receiver of the mobile telephone receives the physiologic data. A software agent, e.g., an application, processes the physiologic data and displays various information related to the physiologic data via, for example, a customized graphical user interface (GUI). In various aspects, the software agent generates displays with a predetermined “look and feel,” i.e., recognizable to a user as belonging to a predetermined group of software programs, GUIs, source devices, communities, gaming software, etc.

0261More particularly, the personal communication system <b>1000</b> includes any environment having therein, or associated with, data or communication of physiologic data for a gaming or recreational purpose. Communication includes any method, act, or vehicle of communication, and/or combinations thereof. For example, communication methods include manual, wired, and wireless. Wireless technologies include radio signals, such as x-rays, ultraviolet light, the visible spectrum, infrared, microwaves, and radio waves, etc. Wireless services include voice and messaging, handheld and other Internet-enabled devices, data networking.

0262Vehicles of communication include the network <b>1008</b>. In various aspects, the network <b>1008</b> comprises local area networks (LAN) as well as wide area networks (WAN) including without limitation Internet, wired channels, wireless channels, communication devices including telephones, computers, wire, radio, optical or other electromagnetic channels, and combinations thereof, including other devices and/or components capable of/associated with communicating data. For example, the communication environments include in-body communications, various devices, various modes of communications such as wireless communications, wired communications, and combinations of the same.

0263Wireless communication modes include any mode of communication between points that utilizes, at least in part, wireless technology including various protocols and combinations of protocols associated with wireless transmission, data, and devices. The points include, for example, wireless devices such as wireless headsets, audio and multimedia devices and equipment, such as audio players and multimedia players, telephones, including mobile telephones and cordless telephones, and computers and computer-related devices and components, such as printers.

0264Wired communication modes include any mode of communication between points that utilizes wired technology including various protocols and combinations of protocols associated with wired transmission, data, and devices. The points include, for example, devices such as audio and multimedia devices and equipment, such as audio players and multimedia players, telephones, including mobile telephones and cordless telephones, and computers and computer-related devices and components, such as printers.

0265In one aspect, the remote node <b>1010</b> comprises social network systems, commercial systems, healthcare systems, pharmacy systems, university systems, financial transaction systems, web communities, physician systems, family caregiver systems, regulatory agency systems, wholesaler/retailer systems as described in U.S. patent application Ser. No. 12/522,249 titled “INGESTIBLE EVENT MARKER DATA SYSTEM,” the disclosure of which is herein incorporated by reference in its entirety. In other aspects, the remote node <b>110</b> comprises state games, behavioral reflective games, psychological response games, synchronization games, actual progress games, and recreational games as described in PCT Patent Application No. PCT/US09/60713 dated Oct. 14, 2009 titled “METHOD AND SYSTEM FOR INCORPORATING PHYSIOLOGIC DATA IN A GAMING ENVIRONMENT” and published as WO 2010/045385, the disclosure of which is herein incorporated by reference in its entirety.

0266In various aspects, a computer system associated with the server <b>4040</b> or the server <b>4040</b> itself described in connection with <figref idref="DRAWINGS">FIG. 82</figref> and/or a computer system associated with the external local node <b>1006</b> and/or the remote node <b>1010</b> may be configured to implement a computer-implemented method as described hereinbelow. The computer system architectural or component comprises one or more processors (such as for instance a microprocessor or microcontroller). The computing system further comprises a storage medium, having operating logic, in coupled to the one or more processors. The computing system further comprises a communication interface coupled to the one or more processors.

0267The processor may be configured to execute the operating logic. The processor may be any one of a number of single or multi-core processors known in the art. The storage may comprise volatile and non-volatile storage media configured to store persistent and temporal copies of the operating logic.

0268In various aspects, the operating logic may be configured to calculate a composite risk score as described hereinbelow. In various aspects, the operating logic may be implemented in instructions supported by the instruction set architecture (ISA) of the processor, or in higher level languages and compiled into the supported ISA. The operating logic may comprise on or more logic units or modules. The operating logic may be implemented in an object oriented manner. The operating logic may be configured to be executed in a multi-tasking and/or multi-threaded manner. In other embodiments, the operating logic may be implemented in hardware, such as a gate array.

0269In various aspects, the communication interface may be configured to facilitate communication between external systems or network-connected systems and the computing system. The communication may include transmission of the at least two distinct digitally watermarked copies and/or the pattern that represents a unique identifier. In various embodiments, the communication interface may be a wired or a wireless communication interface. An example of a wired communication interface may include, but is not limited to, a serial interface, a parallel interface, an Ethernet interface, or a Universal Serial Bus (USB) interface. An example of a wireless communication interface may include, but is not limited to, an interface implementing any variant of the 802.11 wireless protocol, an interface implementing a cellular network protocol, or a Bluetooth interface.

0270For various aspects, the processor may be packaged together with the operating logic. In various aspects, the processor may be packaged together with the operating logic to form a System in Package (SiP). In various embodiments, the processor may be integrated on the same die with the operating logic. In various embodiments, the processor may be packaged together with the operating logic to form a System on Chip (SoC).

0271Accordingly, a computer system associated with the server <b>4040</b> or the server <b>4040</b> itself described in connection with <figref idref="DRAWINGS">FIG. 82</figref> and/or a computer system associated with the external local node <b>1006</b> and/or the remote node <b>1010</b> comprising the above architecture may be configured to implement a computer-implemented method to receive ingestible event marker (IEM) system information from a receiver <b>1004</b> worn by a subject <b>1002</b>, the IEM system information comprising physiological information and information associated with ingestion of medication by the subject <b>1002</b>, wherein the receiver <b>1004</b> is configured to communicate with the computer system, receive contextual information associated with the subject <b>1002</b>, and calculate a composite risk score based on a combination of the IEM system information and the contextual information associated with the subject <b>1002</b>. The composite risk score being calculated in connection with the description associated with <figref idref="DRAWINGS">FIGS. 1-69</figref> herein. The computer-implemented method also may be configured to receive patient history information associated with the subject <b>1002</b> and calculate the composite risk score based on the patient history information.

0272In another aspect, the computer system may be configured to classify the subject <b>1002</b> into a predetermined risk group based on the composite risk score. The computer system also can be configured to dynamically reclassify the subject <b>1002</b> into a predetermined risk group based on the risk score.

0273In another aspect, the computer system may be configured to determine a circadian pattern from the contextual information received from the subject, calculate variability of the circadian pattern, and quantify the variability of the circadian pattern. The computer system also may be configured to determine a dominant circadian pattern, determine a deviation between the dominant circadian pattern and the circadian pattern, calculate a distribution of the deviations, and quantify the distribution of deviations by differential entropy. The computer system also may be configured to determine the dominant circadian pattern using principal component analysis. The computer system also may be configured to determine the composite risk score based at least in part on the variability of the circadian pattern.

0274In another aspect, the a computer system associated with the server <b>4040</b> or the server <b>4040</b> described in connection with <figref idref="DRAWINGS">FIG. 82</figref> and/or a computer system associated with the external local node <b>1006</b> and/or the remote node <b>1010</b> comprising the architecture described above may be configured to implement a computer-implemented method to receive daily data from a subject <b>1002</b>, calculate the daily data into a dominant circadian pattern, track daily variations between the daily data and the dominant circadian pattern, calculate a distribution of the daily variations, and quantify the distribution of the daily variations by differential entropy.

0275In another aspect, the computer system may be configured to calculate a composite risk score based on the quantified distribution of the daily variations. The computer system also may be configured to classify subjects <b>1002</b> into high risk or low risk classes based on the composite risk score. The computer system also can be implemented to temporarily classify subjects <b>1002</b> into high risk or low risk classes based on the composite risk score when the subjects' <b>1002</b> data temporarily increases in variability.

0276In another aspect, the a computer system associated with the server <b>4040</b> or the server <b>4040</b> described in connection with <figref idref="DRAWINGS">FIG. 82</figref> and/or a computer system associated with the external local node <b>1006</b> and/or the remote node <b>1010</b> comprising the architecture described above may be configured to implement a computer-implemented method to receive daily data from a subject <b>1002</b>, apply principal component analysis to the received daily data to determine a dominant mode, determine a variation between the daily data and the dominant mode, and determine how many samples of the daily data are above a predetermined threshold.

0277In another aspect, the computer system may be configured to whiten the variation between the daily data and the dominant mode. The computer system also may be configured to determine how many whitened principal component analysis samples are above the predetermined threshold. The computer system may be configured to determine a variability score based how many samples of the daily data are above the predetermined threshold.

0278In another aspect, the a computer system associated with the server <b>4040</b> or the server <b>4040</b> described in connection with <figref idref="DRAWINGS">FIG. 82</figref> and/or a computer system associated with the external local node <b>1006</b> and/or the remote node <b>1010</b> comprising the architecture described above may be configured to implement a computer-implemented method to receive daily data from a subject for at least one week, extract at least one week of data from the received data, whiten the at least one week of data, and calculate an average deviation between the daily data and the weekly data.

0279The computer system also may be configured to determine a variability score based how many samples of the daily data are above the predetermined threshold. The computer system also may be configured to calculate a composite risk score based on the average deviation between the daily data and the weekly data. The computer system also may be configured to assess a risk associated with a subject based on the composite risk score.

0280The present invention may be defined by way of the following clauses. It will be understood that the features recited are interchangeable defined by the following clauses and their dependencies. That is, the features of the clauses may be combined to define the present invention.

0281Clauses

02821. A computer-implemented method, comprising:

0283receiving, by a computer system, ingestible event marker (IEM) system information from a receiver worn by a subject, the IEM system information comprising physiological information and information associated with ingestion of medication by the subject, wherein the receiver is configured to communicate with the computer system;

0284receiving, by the computer system, contextual information associated with the subject; and

0285calculating, by the computer system, a composite risk score based on a combination of the IEM system information and the contextual information associated with the subject.

02862. The computer-implemented method of Clause 1, comprising:

0287receiving, by the computer system, patient history information associated with the subject; and

0288calculating the composite risk score based on the patient history information.

02893. The computer-implemented method of Clause 1 or Clause 2, comprising classifying the subject into a predetermined risk group based on the composite risk score.

02904. The computer-implemented method of Clause 3, comprising dynamically reclassifying the subject into a predetermined risk group based on the risk score.

02915. The computer-implemented method of any of Clauses 1 to 5, comprising:

0292determining, by the computer system, a circadian pattern from the contextual information received from the subject;

0293calculating, by the computer system, variability of the circadian pattern; and

0294quantifying, by the computer system, the variability of the circadian pattern.

02956. The computer-implemented method of Clause 5, comprising:

0296determining, by the computer system, a dominant circadian pattern;

0297determining, by the computer system, a deviation between the dominant circadian pattern and the circadian pattern;

0298calculating, by the computer system, a distribution of the deviations; and

0299quantifying, by the computer system, the distribution of deviations by differential entropy.

03007. The computer-implemented method of Clause 6, comprising, by the computer system, determining the dominant circadian pattern using principal component analysis.

03018. The computer-implemented method of Clause 5, further comprising determining, by the computer system, the composite risk score based at least in part on the variability of the circadian pattern.

03029. A computer-implemented method, comprising:

0303receiving, by a computer system, daily data from a subject;

0304calculating, by the computer system, the daily data into a dominant circadian pattern;

0305tracking, by the computer system, daily variations between the daily data and the dominant circadian pattern;

0306calculating, by the computer system, a distribution of the daily variations; and

0307quantifying, by the computer system, the distribution of the daily variations by differential entropy.

030810. The computer-implemented method of Clause 9, comprising calculating, by the computer system, a composite risk score based on the quantified distribution of the daily variations.

030911. The computer-implemented method of Clause 10, comprising classifying, by the computer system, subjects into high risk or low risk classes based on the composite risk score.

031012. The computer-implemented method of Clause 10, comprising temporarily classifying, by the computer system, subjects into high risk or low risk classes based on the composite risk score when the subjects' data temporarily increases in variability.

031113. A computer-implemented method, comprising:

0312receiving, by a computer system, daily data from a subject;

0313applying, by the computer system, principal component analysis to the received daily data to determine a dominant mode;

0314determining, by the computer system, a variation between the daily data and the dominant mode; and

0315determining, by the computer system, how many samples of the daily data are above a predetermined threshold.

031614. The computer-implemented method of Clause 13, comprising, by the computer system, whitening the variation between the daily data and the dominant mode.

031715. The computer-implemented method of Clause 14, comprising determining, by the computer system, how many whitened principal component analysis samples are above the predetermined threshold.

031816. The computer-implemented method of Clause 13, comprising determining, by the computer system, a variability score based how many samples of the daily data are above the predetermined threshold.

031917. A computer-implemented method, comprising:

0320receiving, by a computer system, daily data from a subject for at least one week;

0321extracting, by the computer system, at least one week of data from the received data;

0322whitening, by the computer, the at least one week of data; and

0323calculating, by the computer, an average deviation between the daily data and the weekly data.

032418. The computer-implemented method of Clause 17, comprising determining, by the computer system, a variability score based how many samples of the daily data are above the predetermined threshold.

032519. The computer-implemented method of Clause 17 or Clause 18, comprising calculating, by the computer system, a composite risk score based on the average deviation between the daily data and the weekly data.

032620. The computer-implemented method of Clause 19, comprising assessing, by the computer system, a risk associated with a subject based on the composite risk score.

032721. A system, comprising:

0328a computer system, comprising:

0000a processor; and <br /> a memory coupled to the processor, wherein the memory stores program instructions, which when executed from the memory cause the processor to:

0329receive ingestible event marker (IEM) system information from a receiver worn by a subject, the IEM system information comprising physiological information and information associated with ingestion of medication by the subject, wherein the receiver is configured to communicate with the computer system;

0330receive contextual information associated with the subject; and

0331calculate a composite risk score based on a combination of the IEM system information and the contextual information associated with the subject.

033222. The system of Clause 21, wherein the stored program instructions, when executed by the processor, cause the processor to:

0333receive patient history information associated with the subject; and

0334calculate the composite risk score based on the patient history information.

033523. The system of Clause 21, wherein the stored program instructions, when executed by the processor, cause the processor to classify the subject into a predetermined risk group based on the composite risk score.

033624. The system of Clause 23, wherein the stored program instructions, when executed by the processor, cause the processor to dynamically reclassify the subject into a predetermined risk group based on the risk score.

033725. The system of Clause 21, wherein the stored program instructions, when executed by the processor, cause the processor to

0338determine a circadian pattern from the contextual information received from the subject;

0339calculate variability of the circadian pattern; and

0340quantify the variability of the circadian pattern.

034126. The system of Clause 25, wherein the stored program instructions, when executed by the processor, cause the processor to:

0342determine a dominant circadian pattern;

0343determine a deviation between the dominant circadian pattern and the circadian pattern;

0344calculate a distribution of the deviations; and

0345quantify the distribution of deviations by differential entropy.

034627. The system of Clause 26, wherein the stored program instructions, when executed by the processor, cause the processor to determine the dominant circadian pattern using principal component analysis.

034728. The system of Clause 25, wherein the stored program instructions, when executed by the processor, cause the processor to determine the composite risk score based at least in part on the variability of the circadian pattern.

034829. A system, comprising:

0349a computer system, comprising:

0000a processor; and <br /> a memory coupled to the processor, wherein the memory stores program instructions, which when executed from the memory cause the processor to:

0350receive daily data from a subject;

0351calculate the daily data into a dominant circadian pattern;

0352track daily variations between the daily data and the dominant circadian pattern;

0353calculate a distribution of the daily variations; and

0354quantify the distribution of the daily variations by differential entropy.

035530. The system of Clause 29, wherein the stored program instructions, when executed by the processor, cause the processor to calculate a composite risk score based on the quantified distribution of the daily variations.

035631. The system of Clause 29 or Clause 30, wherein the stored program instructions, when executed by the processor, cause the processor to classify subjects into high risk or low risk classes based on the composite risk score.

035732. The system of Clause 29 or Clause 30, wherein the stored program instructions, when executed by the processor, cause the processor to temporarily classify subjects into high risk or low risk classes based on the composite risk score when the subjects' data temporarily increases in variability.

035833. A system, comprising:

0359a computer system, comprising:

0000a processor; and <ul id="ul0003" list-style="none"><li id="ul0003-0001" num="0000"><ul id="ul0004" list-style="none"><li id="ul0004-0001" num="0360">a memory coupled to the processor, wherein the memory stores program instructions, which when executed from the memory cause the processor to:</li></ul></li></ul>

0361receive daily data from a subject;

0362apply principal component analysis to the received daily data to determine a dominant mode;

0363determine a variation between the daily data and the dominant mode; and

0364determine how many samples of the daily data are above a predetermined threshold.

036534. The system of Clause 33, wherein the stored program instructions, when executed by the processor, cause the processor to whiten the variation between the daily data and the dominant mode.

036635. The system of Clause 34, wherein the stored program instructions, when executed by the processor, cause the processor to determine how many whitened principal component analysis samples are above the predetermined threshold.

036736. The system of any of Clauses 33 to 35, wherein the stored program instructions, when executed by the processor, cause the processor to determine a variability score based how many samples of the daily data are above the predetermined threshold.

036837. A system, comprising:

0369a computer system, comprising:

0000a processor; and <ul id="ul0005" list-style="none"><li id="ul0005-0001" num="0000"><ul id="ul0006" list-style="none"><li id="ul0006-0001" num="0370">a memory coupled to the processor, wherein the memory stores program instructions, which when executed from the memory cause the processor to:</li></ul></li></ul>

0371receive daily data from a subject for at least one week;

0372extract least one week of data from the received data;

0373whiten the at least one week of data; and

0374calculate an average deviation between the daily data and the weekly data.

037538. The system of Clause 37, wherein the stored program instructions, when executed by the processor, cause the processor to determine a variability score based how many samples of the daily data are above the predetermined threshold.

037639. The system of Clause 37 or Clause 38, wherein the stored program instructions, when executed by the processor, cause the processor to calculate a composite risk score based on the average deviation between the daily data and the weekly data.

037740. The system of Clause 39, wherein the stored program instructions, when executed by the processor, cause the processor to assess a risk associated with a subject based on the composite risk score.

0378While various details have been set forth in the foregoing description, it will be appreciated that the various aspects of system, apparatus and methods for data collection and assessing outcomes may be practiced without these specific details. For example, for conciseness and clarity selected aspects have been shown in block diagram form rather than in detail. Some portions of the detailed descriptions provided herein may be presented in terms of instructions that operate on data that is stored in a computer memory. Such descriptions and representations are used by those skilled in the art to describe and convey the substance of their work to others skilled in the art. In general, an algorithm refers to a self-consistent sequence of steps leading to a desired result, where a “step” refers to a manipulation of physical quantities which may, though need not necessarily, take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It is common usage to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. These and similar terms may be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities.

0379It is to be understood that this disclosure is not limited to particular aspects described, and as such may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular aspects only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.

0380Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure.

0381Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, representative illustrative methods and materials are now described.

0382All publications and patents cited in this specification are herein incorporated by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference and are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior disclosure. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.

0383Unless specifically stated otherwise as apparent from the foregoing discussion, it is appreciated that, throughout the foregoing description, discussions using terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

0384It is worthy to note that any reference to “one aspect,” “an aspect,” “one aspect,” or “an aspect” means that a particular feature, structure, or characteristic described in connection with the aspect is included in at least one aspect. Thus, appearances of the phrases “in one aspect,” “in an aspect,” “in one aspect,” or “in an aspect” in various places throughout the specification are not necessarily all referring to the same aspect. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more aspects.

0385Although various aspects have been described herein, many modifications, variations, substitutions, changes, and equivalents to those aspects may be implemented and will occur to those skilled in the art. Also, where materials are disclosed for certain components, other materials may be used. It is therefore to be understood that the foregoing description and the appended claims are intended to cover all such modifications and variations as falling within the scope of the disclosed aspects. The following claims are intended to cover all such modification and variations.

0386Some or all of the aspects described herein may generally comprise technologies for various aspects of content traceability using segmented watermark encoding, or otherwise according to technologies described herein. In a general sense, those skilled in the art will recognize that the various aspects described herein which can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or any combination thereof can be viewed as being composed of various types of “electrical circuitry.” Consequently, as used herein “electrical circuitry” includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), electrical circuitry forming a memory device (e.g., forms of random access memory), and/or electrical circuitry forming a communications device (e.g., a modem, communications switch, or optical-electrical equipment). Those having skill in the art will recognize that the subject matter described herein may be implemented in an analog or digital fashion or some combination thereof.

0387The foregoing detailed description has set forth various aspects of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. In one aspect, several portions of the subject matter described herein may be implemented via Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), or other integrated formats. Those skilled in the art will recognize, however, that some aspects of the aspects disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that an illustrative aspect of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution. Examples of a signal bearing medium include, but are not limited to, the following: a recordable type medium such as a floppy disk, a hard disk drive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape, a computer memory, etc.; and a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link (e.g., transmitter, receiver, transmission logic, reception logic, etc.), etc.).

0388As will be apparent to those of skill in the art upon reading this disclosure, each of the individual aspects described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several aspects without departing from the scope of the present disclosure. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

0389Although the foregoing disclosure has been described in some detail by way of illustration and example for purposes of clarity of understanding, it is readily apparent to those of ordinary skill in the art in light of the teachings of this disclosure that certain changes and modifications may be made thereto without departing from the scope of the appended claims.

0390Accordingly, the preceding merely illustrates the principles of the disclosure. It will be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the disclosure and are included within its scope. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the disclosure and the concepts contributed by the inventors to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and aspects of the disclosure as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. The scope of the present disclosure, therefore, is not intended to be limited to the example aspects shown and described herein. Rather, the scope of present disclosure is embodied by the appended claims.

0391Although various aspects have been described herein, many modifications, variations, substitutions, changes, and equivalents to those aspects may be implemented and will occur to those skilled in the art. Also, where materials are disclosed for certain components, other materials may be used. It is therefore to be understood that the foregoing description and the appended claims are intended to cover all such modifications and variations as falling within the scope of the disclosed aspects. The following claims are intended to cover all such modification and variations.