Personalized reduction of hypoglycemia and/or hyperglycemia in a closed loop system

[Cross reference to related applications]

This application claims the benefit of the filing date of U.S. Patent No. 16/789,051, filed February 12, 2020, the entire contents of which are incorporated herein by reference in their entirety. .

Patients with type 1 diabetes may be treated by different methods of insulin delivery. One approach is to manually deliver a correction bolus of insulin to the patient as needed. For example, if a patient's blood glucose level is 170 mg/dL and the target blood glucose level is 120 mg/dL, a 1U bolus may be manually delivered to the patient (with a correction factor of 1:50). ). There are several potential problems with manually delivering such a bolus to a patient. Patients may deliver an inadequate amount of insulin in a bolus. For example, a user may require significantly less insulin than a 1U bolus amount. Once delivered, the insulin is not withdrawn from the patient's bloodstream. As a result, bolus delivery may place the patient at risk of hypoglycemia.

Another approach to insulin is for insulin to be automatically delivered by an insulin pump system. Some insulin pump systems may use a closed loop control system to regulate the amount of insulin delivered at fixed intervals, such as every 5 minutes. The closed-loop algorithm used by the control system may employ a penalty for insulin delivery that is balanced with a penalty for glucose level excursions in the cost function. Use of a cost function typically results in smaller insulin deliveries that are delivered more frequently than manually delivered boluses. A closed-loop system can re-evaluate patient needs more frequently than a manual approach.

In accordance with an exemplary embodiment, the device controls the delivery of insulin from the artificial pancreas to the user. The device includes a monitor interface for interfacing with the glucose monitor to obtain glucose measurements for the user from the glucose monitor. The device may include an artificial pancreas interface for communicating with the artificial pancreas to control the delivery of insulin to the user. The device may further include a processor configured to implement a control loop for controlling the delivery of insulin by the artificial pancreas. The processor may select the insulin delivery dosage for the next delivery among the delivery dosage options that has the best cost function value. The cost function may have a glucose cost component that reflects the difference between a predicted glucose level and a target glucose level for the user to create a dosage option for the user. The cost function may have an insulin cost component that reflects how different the dosage option is from the current reference insulin dosage. Additionally, the cost function may have a glucose cost weighting factor for weighting the glucose cost component and an insulin cost weighting factor for weighting the insulin cost component. At least one of the glucose cost weighting factor and the insulin cost weighting factor may have a value customized for the user.

According to an exemplary implementation, the method is performed by a processor. According to a method, a glucose measurement for a user is received from a glucose monitor. Determine the dosage for the next delivery of insulin from the artificial pancreas to the user. The determining step includes applying a cost function to multiple possible doses of insulin to the user and selecting one of the possible doses of insulin that has the best cost under the cost function. include. The cost function includes a glucose cost element that reflects the difference between the predicted glucose level and the target glucose level for the user to create a dosage option for the user, and a glucose cost component that reflects the difference between the predicted glucose level and the target glucose level for the user, and how much the dosage option is relative to the current baseline insulin dosage. Having different insulin cost factors to reflect. The cost function has a glucose cost weighting factor for weighting the glucose cost component and an insulin cost weighting factor for weighting the insulin cost component. The glucose cost weighting factor and the insulin cost weighting factor have values customized for the user. The artificial pancreas is instructed to deliver the selected dosage to the user.

A non-transitory computer-readable storage medium can store computer-readable instructions that cause a processor to perform a method.

The processor can instruct the artificial pancreas through the artificial pancreas interface to deliver the selected insulin delivery dosage. In some examples, only one of the glucose cost weighting factor and the insulin cost weighting factor has a value customized for the user. In other examples, both the glucose cost weighting factor and the insulin cost weighting factor have values customized for the user.

The glucose cost weighting factor may have the value of the baseline glucose cost weighting factor multiplied by a value indicating the ratio of the custom value representing the user's insulin needs to the reference value representing the insulin needs. The value indicating the ratio may be a value of an index of the ratio. The glucose cost weighting factor may have a value of a reference glucose cost weighting factor multiplied by a value indicating the ratio of a reference value representing insulin needs to a custom value representing insulin needs of the user.

The insulin weighting factor may have the value of the baseline insulin cost weighting factor multiplied by a value indicating the ratio of the custom value representing the user's insulin needs to the reference value representing the insulin needs. The insulin cost weighting factor may be the value of the baseline insulin cost weighting factor multiplied by a value indicating the ratio of the reference value representing the user's insulin needs to the custom value representing the user's insulin needs.

The artificial pancreas interface may be a wireless communication interface. The device may be one of a mobile computing device, a smartphone or an insulin pump component. The processor may enforce boundaries on the parameters used in determining at least one of the glucose cost weighting factor or the insulin cost weighting factor that is customized for the user. The processor determines at least one of the glucose cost weighting factor or the insulin cost weighting factor based on at least one of a correction factor for insulin sensitivity for the user, an insulin to carbohydrate ratio for the user, or a basal insulin level for the user. May be configured to determine one. At least one of the glucose cost weighting factor and the insulin cost weighting factor may have a value customized for a TDI (Total Daily Insulin) user.

<figref num="1A">Figure 2 depicts a simplified block diagram of an illustrative artificial pancreas system.</figref><figref num="1B">2 depicts a flowchart of steps that may be performed by a control loop of an artificial pancreas system.</figref><figref num="2">A more detailed representation of the artificial pancreas system is provided to aid in explanation.</figref><figref num="3">Represents a schematic representation of possible types of management devices.</figref><figref num="4">Represents a flowchart of illustrative steps for calculating costs with a cost function.</figref><figref num="5">2 depicts a flowchart of illustrative steps that may be performed to determine glucose cost weighting factors.</figref><figref num="6">2 depicts a flowchart of illustrative steps that may be performed to determine an insulin cost weighting factor.</figref><figref num="7">FIG. 6 depicts an illustrative plot of a blood glucose level graph and an insulin delivery graph for an example of a user with a very high TDI. FIG.</figref><figref num="8">FIG. 4 depicts an illustrative plot of a blood glucose level graph and an insulin delivery graph for an example of a user with a low TDI. FIG.</figref><figref num="9">2 depicts a flowchart of illustrative steps that may be performed using bounds on the parameters used in the penalty function.</figref>

One difficulty with traditional closed-loop approaches to insulin delivery is that the approach does not account for differences in patients' daily insulin needs. For example, it is possible to evaluate penalties for all users (e.g., patients) without The consequences of this traditional approach can be problematic for users with non-standard daily insulin needs, such as users with high or low insulin needs. Exemplary embodiments attempt to solve this problem by customizing the cost function to a user's daily insulin needs using clinical parameters that express their daily insulin needs. . Specifically, the rate at which one or more penalties are applied may be modified. If daily insulin needs are high, the ratio can be biased with the aim of penalizing glucose excursions more and insulin excursions less. When daily insulin needs are low, the ratio can be biased with the aim of penalizing insulin excursions more and glucose excursions less.

The exemplary embodiments described herein relate to closed loop artificial pancreas (AP) systems. Closed-loop AP systems seek to automatically and continuously control a user's BG (blood glucose) levels by mimicking the endocrine functionality of a healthy pancreas. AP systems use a closed loop control system with a cost function. The penalty function helps throttle the rate of insulin infusion to try to avoid hypoglycemia and hyperglycemia. However, unlike conventional systems that use common or reference parameters for a user's insulin needs in the cost function, example embodiments provide a customized system in the cost function that reflects the user's individualized insulin needs. Parameters can be used. The use of customized parameters results in a cost function that over time results in insulin dosages that are more suited to the user's individualized insulin needs. This helps smooth the user's response to insulin injection and helps better avoid hypoglycemia and hyperglycemia.

In an example, the AP application monitors the user's glucose values and includes the monitored glucose values (e.g., BG concentration or BG measurements) and other information, such as carbohydrate intake, meal frequency, or may be executed by the processor to enable the system to determine an appropriate level of insulin for the user and maintain the user's BG value within an appropriate range, based on the same or the like. The appropriate BG value range may be considered a target BG value for a particular user. For example, a target BG value may be accepted if it falls within the range of 80 mg/dL to 120 mg/dL, a range that meets clinical standards of care for the treatment of diabetes. However, AP applications such as those described herein take account of the user's activity level to more accurately establish the target BG value. for) and the target BG value may be set to, for example, 110 mg/dL or similar. As described in more detail with reference to examples herein, the AP application utilizes the monitored BG values and other information to provide instructions to the user, such as to a wearable drug delivery device, including a pump. Commands can be generated and sent to control insulin delivery, vary future doses or timing, and control other functions.

FIG. 1A illustrates a simplified block diagram of an example AP system 100 suitable for practicing example embodiments. The example AP system 100 may include a controller 102, a pump mechanism or other fluid extraction mechanism 104 (hereinafter pump 104), and a sensor 108. Controller 102, pump 104, and sensor 108 may be communicatively coupled to each other through wired or wireless communication paths. For example, each controller 102, pump 104 and sensor 108 may include a wireless radio frequency transceiver operative to communicate through one or more communication protocols, such as Bluetooth or the like. May be equipped. The sensor 108 may be a glucose monitor such as a continuous glucose monitor (CGM) 108. CGM 108 may operate, for example, to measure a user's BG value to generate measured actual BG level signal 112.

As shown in the example, the controller 102 can receive a desired BG level signal 110, which can be a first signal indicating a desired BG level or range to a user. . The desired BG level signal 110 may be received from a user interface to a controller or other device, or by an algorithm that automatically determines the BG level for the user. Sensor 108 may be coupled to the user and operative to measure an approximation of the user's actual BG level. The measured BG value, the actual BG level, and the measured approximation of the actual BG level are only approximations of the user's BG level, and there may be errors in the measured BG level. should be understood. The error may be due to several factors, such as the age of the sensor 108, the location of the sensor 108 on the user's body, environmental factors (eg, altitude, humidity, barometric pressure), or the like. The terms measured BG value, actual BG level, and measured approximation of actual BG level may be used interchangeably throughout the specification and drawings. In response to the measured BG level or value, sensor 108 generates a signal indicative of the measured BG value. As shown in the example, controller 102 may receive a measured BG level signal 112 from sensor 108 over a communication path, where measured BG level signal 112 approximates the user's actual BG level. It can be a second signal indicating the measured value.

Based on the desired BG level signal 110 and the measured actual BG level signal 112, the controller 102 can generate one or more control signals 114 to direct operation of the pump 104. For example, one of the control signals 114 may cause the pump 104 to deliver a dose of insulin 116 to the user through the output 106. A dose of insulin 116 may be determined based on the difference between desired BG level signal 110 and actual BG level signal 112, for example. The aforementioned penalty function serves to determine dosage as part of a closed loop control system that will be described below. The dose of insulin 116 may be determined as the appropriate amount of insulin to drive the user's actual BG level to the desired BG level. Based on operation of pump 104, as determined by control signal 114, a user may receive insulin 116 from pump 104.

In various examples, one or more elements of AP system 100 may be incorporated into a wearable or on-body drug delivery system attached to a user.

FIG. 1B depicts a flowchart 130 of steps that may be performed by an exemplary implementation of an AP system in determining what dose of insulin to deliver to a user as part of a closed loop control system. First, a BG level measurement is obtained by sensor 108 (132), as described above in connection with FIG. 1A above. The BG level measurement is transmitted to controller 102 via signal 112 (134). Controller 102 calculates an error value as the difference between measured BG level 112 and desired BG level 110 (136). A closed-loop control system aggregates the cost function penalty over a wide range of possible dosages. penalty). The cost function is applied to the possible dosages and the dosage with the best penalty function value is selected (138). Depending on how the penalty function is constructed, the best value may be the lowest value or the highest value. The penalty function used in the exemplary embodiment is described further below. A control signal 114 may be generated by controller 102 and sent to pump 104 to cause the pump to deliver a desired insulin dose to the user (140).

A simplified block diagram of an example AP system 100 provides a general description of the operation of the system. A more detailed implementation example of an apparatus that can be used in such an AP system is illustrated in FIG.

Various examples of AP systems include wearable drug delivery devices that may operate in a system for treating a diabetic user according to a diabetes treatment plan. A diabetes treatment plan may include several parameters regarding insulin delivery that may be determined and modified by a computer application referred to as an AP application.

A wearable drug delivery device as described herein may include a controller operative to direct operation of the wearable drug delivery device through an AP application. For example, a control device of a wearable drug delivery device can provide a user with selectable activity modes of operation. Operation of the drug delivery device in an active mode of operation can reduce the probability of hypoglycemia when insulin sensitivity increases for the user and reduce the probability of hyperglycemia when insulin requirement increases for the user. be able to. The active mode of operation may be activated by the user or automatically by the control device. The controller may automatically activate the activity mode of operation based on the detected user activity level and/or the detected user location.

FIG. 2 illustrates an example of a drug delivery system. The drug supply system 200 may include a drug supply device 202, a management device 206, and a BG sensor 204.

In the example of FIG. 2, drug delivery device 202 may be a wearable or an on-body drug delivery device worn by the user on the user's body. As shown in FIG. 2, the drug supply device 202 may include an IMU (inertial measurement unit) 207. Drug delivery device 202 may further include a pump mechanism 224 and a needle placement mechanism 228, which may be referred to as drug extraction mechanisms or elements in some examples. In various examples, pump mechanism 224 may include a pump or plunger (not shown).

Needle placement element 228 includes, for example, a needle (not shown), a cannula (not shown), and any other fluid conduit elements for coupling liquid medicine stored in tank 225 to a user. good. The cannula may form part of the fluid line element that couples the user to the tank 225. After needle placement element 228 is activated, a fluid line (not shown) is provided to the user, and pump mechanism 224 pumps liquid medication from tank 225 for supplying the liquid medication to the user through the fluid line. can be released. The fluid conduit may include, for example, tubing (not shown) coupling wearable drug delivery device 202 to a user (eg, tubing coupling a cannula to tank 225).

Wearable medicine supply device 202 may further include a control device 221 and a communication interface device 226. Control device 221 may be implemented in hardware, software, or any combination thereof. The control device 221 may be, for example, a microprocessor, a logic circuit, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC) or a microcontroller coupled to a memory. Controller 221 may maintain date and time as well as other functions performed by the processor (eg, calculations or the like). Controller 221 may be operative to execute AP algorithms stored in memory that enable controller 221 to direct the operation of drug delivery device 202. In addition, the control device 221 receives information not only from the IMU 207 but also from the GPS (global positioning any other sensors on the drug delivery device 202, such as those on the management device 206 or CGM 204 (e.g., accelerometers, location services applications, or the like), ), or any sensor coupled to drug delivery device 202, may be operative to receive data or information indicative of user activity.

The controller 221 processes data from the IMU 207 or any other coupled sensor to determine whether an alarm or other communication should be issued to the user and/or the user's caregiver or to the drug delivery device 202. It can be determined whether the operating mode should be adjusted. Control device 221 can provide alerts via communication interface device 226, for example. The communication interface device 226 provides a communication link to one or more management devices that are physically separate from the drug delivery device 202, including, for example, the management device 206 of a user and/or a caregiver of a user (e.g., a patient). can be provided. The communication link provided by communication interface device 226 may include any wired or wireless communication link operating according to any known communication protocol or standard, such as Bluetooth or cellular standards.

The example of FIG. 2 further displays a drug delivery device 202 associated with a BG sensor 204, which may be, for example, a CGM. CGM 204 may be physically separate from drug delivery device 202 or may be an integrated element thereof. CGM 204 can provide data to controller 221 indicating the user's measured or detected BG level.

Management device 206 may be maintained and operated by the user or the user's caregiver. The management device 206 may control the operation of the drug delivery device 202 and/or may be used to review data or other information indicative of the operating status of the drug delivery device 202 or the status of the user. Management device 206 may be used to direct the operation of drug delivery device 202. Management device 206 may include a processor 261 and a memory device 263. Memory device 262 can store an AP application 269 that includes programming code that can implement an active mode, a hyperglycemic protection mode, and/or a hypoglycemic protection mode. The management device 206 may receive alerts, notifications, or other communications from the drug delivery device 202 through one or more known wired or wireless communication standards or protocols.

The drug delivery system 200 implements an AP application that includes functionality to determine the movement of the wearable drug delivery device indicative of the user's physical activity, including activity mode, hyperglycemia mode, hypoglycemia mode, and wearable drug delivery device. It may also operate to perform other functions such as controlling the feeding device. Drug delivery system 200 may be an automated drug delivery system that may include a wearable drug delivery device (pump) 202, a sensor 204, and a personal diabetes management device (PDM) device 206.

In an example, the wearable drug delivery device 202 may be attached to the body of the user 205 and may supply the user with any therapeutic agent, including any drug or drug, such as insulin or the like. Wearable medicine supply device 202 may be, for example, a wearable device worn by a user. For example, wearable drug delivery device 202 may be coupled directly to a user (eg, attached directly to a part of the user's body and/or skin by adhesive or the like). In examples, the surface of wearable drug delivery device 202 may include an adhesive to facilitate attachment to the user.

Wearable medication delivery device 202 may be referred to as a pump or insulin pump with respect to its operation of dispensing medication from medication delivery tank 225 to a user.

In examples, the wearable drug delivery device 202 includes a tank 225 for storing drugs (such as insulin), a needle or a needle for delivering drugs to the user's body (which may be done subcutaneously, intraperitoneally, or intravenously). It may include a cannula (not shown), a pump mechanism 224 or other drive mechanism to move medication from the tank 225 to the user via the needle or cannula (not shown). Tank 225 may be configured to store or hold a liquid or fluid, such as insulin, morphine, or another therapeutic agent. Pump mechanism 224 may be fluidly coupled to tank 225 and communicatively coupled to processor 221. Wearable drug delivery device 202 may include a battery, piezoelectric device, etc. to power pump mechanism 224 and/or other elements (such as processor 221, memory 223, and communication device 226) of wearable drug delivery device 202. ) or the like (not shown). Similarly, although not shown, a power supply device (electrical power supply) may similarly be included in each of the sensor 204, smart accessory device 207, and management device (PDM) 206.

In an example, BG sensor 204 may be a device communicatively coupled to processor 261 or 221 and operative to measure BG values at predetermined time intervals, such as every 5 minutes or the like. good. BG sensor 204 may provide several BG measurements to an AP application running on a separate device. For example, BG sensor 204 may be a continuous BG sensor that provides BG measurements to an AP application running on a separate device periodically, such as approximately every 5, 10, 12 minutes, or the like. good.

Wearable drug delivery device 202 may also include an IMU 207. IMU 207 may operate to detect various motion parameters (eg, acceleration, deceleration, speed, direction such as roll, pitch, yaw, compass heading, or the like) that may be indicative of user activity. For example, IMU 207 can output a signal in response to detecting movement of wearable drug delivery device 202 that is indicative of any physical condition of the user, such as movement or position of the user. Based on the detected user activity, the drug delivery device 202 can adjust its operations regarding drug delivery, for example, by implementing an activity mode, as discussed herein.

When the wearable drug delivery device 202 is operating in a normal mode of operation, the information provided by the sensor 204 and/or management device (PDM) 206 (e.g., blood glucose measurements, IMU, GPS enabled the insulin stored in the tank 225).

For example, wearable drug delivery device 202 may be implemented as a controller 221 (or processor) for controlling the delivery of a drug or therapeutic agent, and may contain analog and/or digital circuitry. The electrical circuitry used to implement processor 221 may include separate specialized logic and/or components, application specific integrated circuits (e.g., APs stored within memory 223), App229) may include a microcontroller or processor that executes software instructions, firmware, programming instructions, or programming code, or any combination thereof. For example, the processor 221 may include a control algorithm, such as an AP application 229, and a control algorithm that may cause the processor 221 to administer drugs or treatments to the pump at predetermined intervals or when needed to bring the BG readings to the target BG value. Other programming code may be executed that may cause the processor 221 to deliver a dose of medication. Dose size and/or timing may be determined between the wearable medication delivery device 202 and the administration device 206 or other device, such as a computing device at a healthcare provider facility, using a wired or wireless link, such as 220. may be programmed, for example, within the AP application 229, by the user or by a third party (such as a healthcare provider, wearable drug delivery device manufacturer, or the like). In the example, the pump or wearable drug delivery device 202 is communicatively coupled to the management device processor 261 through a wireless link 220 or through a wireless link such as 291 from the smart accessory device 207 or 208 from the sensor 204. Pump mechanism 224 of the wearable drug delivery device can receive an actuation signal from processor 261 and operate to release insulin from reservoir 225 or the like in response to receiving the actuation signal.

Devices within system 200, such as management device 206, smart accessory device 207, and sensors 204, may operate to perform various functions, including controlling wearable drug delivery device 202. For example, management device 206 may include a communication device 264, a processor 261, and management device memory 263. Management device memory 263 may store example AP applications 269 that include programming code that, when executed by processor 261, provides example processes described herein. Management device memory 263 may store programming code to provide examples of the processes described with reference to examples herein.

Although not shown, the system 200 may include, for example, the Apple Watch, other wearable smart devices including glasses provided by other manufacturers, GPS-enabled wearables, wearable fitness devices, smart clothing, or It may include smart accessory devices, which may be similar. Similar to management device 206, smart accessory devices (not shown) may operate to perform various functions, including controlling wearable medication delivery device 202. For example, smart accessory devices may include communication devices, processors, and memory. The memory may store example AP applications that include programming code for providing examples of the processes described with reference to the examples described herein. The memory may store programming code and may operate to store data regarding the AP application.

Sensor 204 of system 200 may be the CGM described above, which may include a processor 241 , memory 243 , sensing or measurement device 244 , and communication device 246 . Memory 243 may be operative to store examples of AP application 249 and to store data regarding AP application 249 as well as other programming code. AP application 249 may include programming code to provide examples of the processes described with reference to the examples described herein.

Instructions for determining the delivery (e.g., the size and/or timing of any dose of the drug or therapeutic agent) of the drug or therapeutic agent (e.g., as a bolus dosage) to the user are provided by the wearable drug delivery device 202. It may be generated locally or remotely generated and provided to wearable drug delivery device 202. In an example of a local determination of drug or therapeutic agent delivery, programming instructions, such as an example AP application 229, stored in memory 223 coupled to wearable drug delivery device 202 cause wearable drug delivery device 202 to make a decision. can be used to lower Additionally, wearable drug delivery device 202 is operable to communicate with wearable drug delivery device 202 through communication device 226 and communication link 288 and with BG sensor 204 through communication device 226 and communication link 289.

Alternatively, remote instructions may be provided by a management device (PDM) 206 to the wearable drug delivery device 202 over a wired or wireless link. PDM 206 may be equipped with a processor 261 that may execute instances of AP applications 269 when present in memory 263. Memory may store computer readable instructions for execution by processor 261. Memory may include non-transitory computer-readable storage media for storing instructions executable by a processor. Wearable drug delivery device 202 may execute any received commands (internal or originating from management device 206) for the delivery of insulin to the user. In this way, the delivery of insulin to the user can be automated.

In various examples, wearable medication delivery device 202 can communicate with management device 206 through wireless communication link 288. The management device 206 may be an electronic device such as a smartphone, tablet, dedicated diabetes care management device, or the like. Alternatively, management device 206 may be a wearable wireless accessory device, such as a smart watch or the like. Wireless links 287-289 may be any type of wireless link provided by any known wireless standard. By way of example, the wireless links 287-289 can be connected to a wearable drug delivery device, e.g. based on Bluetooth, Wi-Fi, near-field communication standards, cellular standards, or any other wireless optical or radio frequency protocols. 202, the management device 206, and the sensor 204.

Sensor 204 may be coupled to the user, eg, by adhesive or the like, and may provide information or data regarding one or more medical conditions and/or physical characteristics of the user. Information or data provided by sensor 204 may be used to adjust drug delivery operations of wearable drug delivery device 202. For example, sensor 204 may be a glucose sensor that operates to measure BG and output a BG value or data representative of a BG value. For example, sensor 204 may be a glucose monitor that periodically provides BG measurements to a CGM, or another type of device or sensor that provides BG measurements.

Sensor 204 may include a processor 241, memory 243, sensing/measuring device 244, and communication device 246. The communication device 246 of the sensor 204 may include an electronic transmitter, receiver, and/or transceiver for communicating with the management device 206 on the wireless link 222 or the wearable drug delivery device 202 on the link 208. Sensing/measuring device 244 may include one or more sensing elements, such as a BG measurement element, a heart rate monitor, a blood oxygen sensor element, or the like. Processor 241 may include a separate specialized logic and/or component, an application specific integrated circuit, software instructions, firmware, a microcontroller or processor that executes programming instructions stored in memory (such as memory 243); or any combination thereof. For example, memory 243 may store examples of AP applications 249 executable by processor 241.

Although sensor 204 is depicted as separate from wearable drug delivery device 202, in various examples sensor 204 and wearable drug delivery device 202 may be incorporated within the same unit. That is, in one or more examples, sensor 204 may be part of wearable drug delivery device 202 and may be housed within the same housing of wearable drug delivery device 202 (e.g., sensor 204 may be part of wearable drug delivery device 202). (may be located or embedded within the drug delivery device 202). Glucose monitoring data (e.g., measured BG values) determined by sensor 204 may be provided to wearable medication delivery device 202, smart accessory device 207, and/or management device 206, which may can be used to determine movements of the wearable drug delivery device indicative of the user's physical activity, activity mode, hyperglycemia mode, and hyperglycemia mode.

In an example, management device 206 may be a personal diabetes manager. PDM 206 may be used to program or adjust the operation of wearable drug delivery device 202 and/or sensor 204. The management device 206 may be any portable electronic device including, for example, a dedicated controller such as a processor 261, a smartphone or a tablet. In the example, management device (PDM) 206 may include a processor 261, management device memory 263, and communication device 264. The management device 206 may be implemented as a processor 261 (or control device) for performing processing to handle the user's BG level and for controlling the delivery of medicines or therapeutics to the user, and may be an analog and/or or may house digital electronic circuitry. Processor 261 may operate to execute programming code stored within management device memory 263. For example, management device memory 263 may operate to store AP applications 269 that may be executed by processor 261. When processor 261 executes AP application 269, it may operate to perform a variety of functions, such as those described in connection with the example. Communication device 264 may be a receiver, transmitter, or transceiver that operates according to one or more radio frequency protocols. For example, communication device 264 may include a cellular transceiver and a Bluetooth transceiver, such that management device 206 can communicate with a data network through the cellular transceiver and with sensor 204 and wearable drug delivery device 202. It can be done. Separate transceivers of communication device 264 may be operative to transmit signals containing information usable or generated by an AP application or the like. The communication devices 226 and 246 of the individual wearable drug delivery device 202 and sensor 204, respectively, may be operated to transmit signals containing information usable or generated by an AP application or the like. .

Wearable drug delivery device 202 may communicate with sensor 204 over wireless link 208 and with management device 206 over wireless link 220. Sensor 204 and management device 206 may communicate over wireless link 222. Smart accessory device 207, when present, may communicate with wearable drug delivery device 202, sensor 204, and management device 206 over wireless links 287, 288, and 289, respectively. Wireless links 287, 288, and 289 may be any type of wireless link that operates using known wireless standards or proprietary standards. By way of example, wireless links 287, 288 and 289 provide communication links based on Bluetooth, Wi-Fi, near field communication standards, cellular standards or any other wireless protocol through individual communication devices 226, 246 and 264. You may do so. In some examples, wearable drug delivery device 202 and/or management device 206 each include a user interface 227 and 268, such as a keypad, touch screen display, lever, button, microphone, speaker, display, or the like. It operates to allow the user to enter information and to allow the management device to output information for presentation to the user.

In various examples, drug delivery system 200 may be an insulin drug delivery system. For example, the wearable drug delivery device 202 may be the OmniPod® (Insulet Corporation, Billerica, MA) insulin delivery device described in U.S. Pat. No. 7,303,549, U.S. Pat. No. 7,137,964, or U.S. Pat. , each of which is incorporated herein by reference in its entirety.

In an example, drug delivery system 200 implements an AP algorithm (and/or or provide AP functionality). AP applications may be implemented by wearable drug delivery device 202 and/or sensor 204. The AP application may be used to determine the frequency and dosage of insulin deliveries. In various examples, the AP application uses information known about the user, such as the user's gender, age, weight, or height, and/or information gathered (e.g., from sensor 204) about the user's physical characteristics or condition. Based on this, the frequency and dosage for feeding may be determined. For example, the AP application may determine the appropriate delivery of insulin based on the user's glucose level monitored via sensor 204. The AP application may allow the user to adjust insulin delivery. For example, the AP application can configure the wearable drug delivery device's modes such as activity mode, hyperglycemia protection mode, hypoglycemia protection mode, commands to deliver insulin boluses, or similar things to the wearable. A user may be able to select (eg, through input) commands for output to drug delivery device 202. In one or more examples, different functions of the AP application may be distributed between two or more of the management device 206, wearable drug delivery device (pump) 202, or sensor 204. In other examples, different functions of the AP application may be performed by one device, such as the management device 206, the wearable drug delivery device (pump) 202, or the sensor 204. In various examples, drug delivery system 200 may be configured as described in U.S. patent application Ser. Both patent applications are incorporated herein by reference in their entirety.

As described herein, drug delivery system 200, or any element thereof, such as a wearable drug delivery device, may be considered to provide AP functionality or implement an AP application. Accordingly, references to the AP application (e.g., its functionality, operation or capabilities) are made for convenience and may include the drug delivery system 200 or any components thereof (e.g., the wearable drug delivery device 202 and/or the management device 206). ) operations and/or functionality may be referred to and/or included. Drug delivery system 200 - e.g., an insulin delivery system implementing an AP application - may be a drug delivery system or an AP application-based delivery system that uses sensor input (e.g., data collected by sensor 204). It is conceivable.

In the example, the drug dispensing device 202 includes a communication device 264, which may enable individual devices to communicate with the cloud-based service 211, such as Bluetooth, Wi-Fi, near-field communication standards, cellular It may be a receiver, transmitter, or transceiver that operates according to one or more radio frequency protocols, such as standards. For example, output from sensor 204 or wearable drug delivery device (pump) 202 may be transmitted through a transceiver of communication device 264 to cloud-based service 211 for storage or processing. Similarly, wearable medication delivery device 202, management device 206, and sensor 204 may be operative to communicate with cloud-based service 211 through communication link 288.

In the example, a separate receiver or transceiver of each separate device 202, 206, 207 is configured to receive a signal containing a number of separate BG measurements that may be transmitted by sensor 204. It may work. A separate processor of each separate device 202, 206 or 207 may be operative to store each separate BG measurement in a separate memory such as 223, 263 or 273. Individual BG measurements may be stored as data for AP algorithms such as 229, 249 or 269. In a further example, an AP application running on any management device 206, smart accessory device 207, or sensor 204 sends control signals for reception by the wearable drug delivery device to a separate communication device 264, 274, 246. may operate to transmit through a transceiver implemented by. In an example, the control signal may indicate the amount of insulin released by wearable drug delivery device 202.

In examples, one or more devices 202, 204, or 206 may be operative to communicate through wired communication links 277, 278, and 279, respectively. Cloud-based services (not shown) may utilize servers and data storage (not shown). The communication link coupling drug delivery system 200 to a cloud-based service may be a cellular link, a Wi-Fi link, a Bluetooth link, or a combination thereof established between individual devices 202, 204, or 206 of system 200. It's fine. For example, data storage (not shown) provided by a cloud-based service may store anonymized data such as a user's weight, BG measurements, age, food carbohydrate information, or the like. In addition, cloud service 211 may process anonymized data from multiple users to provide generalized information regarding various parameters used by AP applications. For example, a general target BG value based on activity level, age, for a particular exercise or sport may be calculated from anonymized data, which indicates that the user is in activity mode (or hyperglycemia protection mode). , or hypoglycemia protection mode) or when the system 200 automatically implements the active mode (or hyperglycemia protection or hypoglycemia protection mode). Cloud-based services may provide processing services for system 200, such as performing the processing described with reference to later examples.

Wearable drug delivery device 202 may include a user interface 227. User interface 227 may include any mechanism for a user to enter data into drug delivery device 202, such as, for example, buttons, knobs, switches, touch screen displays, or any other user interaction elements. User interface 227 may include any mechanism to relay data to the drug delivery device 202, such as a display, a touch screen display, or a visual, audible, or tactile (e.g., vibratory) Any means for providing an output (eg, as an alert) may be included. User interface 227 may include additional elements not specifically shown in FIG. 2 for the sake of brevity and explanation. For example, user interface 227 may include one or more user input or output elements for receiving input from or providing output to a user or caregiver (e.g., a parent or nurse). , a display that outputs a visual alarm, a speaker that outputs an audible, or a vibration that outputs a tactile indicator to alert the user or caregiver of a potential activity mode, power source (e.g., battery), etc. may include equipment. Input to user interface 227 may be through, for example, a fingerprint sensor, tactile input sensor, buttons, touch screen display, switch, or the like. In yet another alternative, active mode operation may be requested via the management device 206 that is communicatively coupled to the controller 221 of the wearable drug delivery device 202. Generally, a user can generate instructions that may be stored in memory, such as 223 or 226, as a user preference, which specifies when system 200 should enter an active mode of operation.

Examples of various operational scenarios and processes performed by system 200 are described herein. For example, system 200 may operate to implement example processes for active modes including hyperglycemia protection mode and hypoglycemia protection mode, as described in further detail below.

In examples, the drug delivery device 202 may operate as an AP system (e.g., as part of the AP system 100) and/or provide functionality with respect to substantially all aspects of the AP system, or at least portions thereof. The techniques or algorithms may be implemented through the controlling and providing AP application. Accordingly, references herein to an AP system or an AP algorithm refer to techniques or algorithms implemented by an AP application running on drug delivery device 202 to provide the features and functionality of an AP system. You may do so. Drug delivery device 202 may operate in an open-loop or closed-loop manner to provide insulin to a user.

Additional features may be implemented as part of the AP application, such as activity mode, hyperglycemia mode, hypoglycemia mode, or the like. For example, when the programming code is executed, the drug delivery device 202 enables an active mode, a hyperglycemic mode, a hypoglycemic mode, or the like of the AP application. When an AP application that includes programming code for an activity mode, a hyperglycemia mode, and a hypoglycemia mode is executed, the AP application may detect motion or movement of the wearable drug delivery device that is indicative of the user's physical activity. You may adjust your actions. For example, movements and movements of the wearable drug delivery device 202 that induce movements specific to a user's physical activity (e.g., movements such as jumping, dancing, running, weight lifting, cycling, or the like) are controlled by the IMU 207. can be detected. Additionally, IMU 207 may include a GPS that can detect the location of wearable drug delivery device 202, as described with reference to FIG. Alternatively or additionally, wearable medication delivery device 202 may utilize Wi-Fi location services to determine the location of wearable medication delivery device 202. For example, the AP algorithm may learn from repeated interactions with users, who may input instructions at particular times, that they are about to perform a physical activity. Alternatively, or in addition, the wearable drug delivery device 202 detects a particular location (e.g., a gym, sports field, stadium, track, or the like) and detects a location where the user attempts to increase their physical activity. You may decide to do so.

The management device (eg, see 206 in FIG. 2) can take many different forms. FIG. 3 shows a diagram 300 illustrating different possible configurations for the management device 302. For example, management device 302 may be implemented within smartphone 304. Benefits of using a smartphone 304 as the management device 302 include that the user typically already owns a smartphone 304 and the AP application can be installed on the smartphone 304 immediately. Management device 302 may be a custom control device 306, as described above. Management device 302 may be a mobile computing device 308, such as a tablet computer, laptop computer, wearable computing device, or the like. Finally, management device 302 may be another type of computing device 310, such as a desktop computing device.

To appreciate the value of using customized parameters in the penalty function of example embodiments, it is useful to review common cost functions that may be used in insulin delivery systems. The general cost function is<img file="JP7463533B2_D0001.tif" />may be expressed as, where J is the total penalty, I_recis the current recommended insulin supply evaluated for the summed penalty, Q is the glucose excursion coefficient, f(I_rec) is any general function that associates this recommended insulin delivery with the corresponding predicted glucose value, G_targetis the current control target, R is the insulin excursion coefficient, I_bis the current baseline insulin supply, n and m are general coefficients representing arbitrary scaling of penalties for glucose and/or insulin excursions.

term<img file="JP7463533B2_D0002.tif" />may be considered the glucose cost of providing the recommended dose of insulin. Function f(I_rec) is a function that associates the recommended insulin delivery dosage with the user's corresponding predicted glucose level. Therefore, there is a penalty for glucose levels that are not at the target level. term<img file="JP7463533B2_D0003.tif" />may be considered the insulin cost of providing the recommended dose of insulin. There is a penalty for insulin delivery dosages that vary from the basal dosage. Q may be considered a glucose cost weighting function for weighting glucose costs, and R may be considered an insulin cost weighting factor for weighting insulin costs. The values of n and m may be set to 2 in many cases.

The coefficients Q and R are fixed for all users in the general case. Therefore, if R is fixed at 1000 and quadratic scaling is used, an insulin excursion of 2U above basal may have a penalty of 4000 for all users. This includes users whose daily insulin needs vary. For a user with a TDI (total daily insulin) need of 10U, a supply of 2U represents a supply of 20% of the total insulin required per day. In contrast, for the use of 100U of TDI, a supply of 2U represents a supply of 2% of the user's daily insulin needs. Therefore, the same penalty due to R coefficient weight may result in insulin delivery doses representing very different amounts of TDI for individual users.

If the value of Q is fixed at 10, a glucose excursion of 50 mg/dL above the glucose target results in a cost of 25,000 for both users mentioned above. This is despite the fact that one user with a TDI of 10 U requires 10 times less insulin to cause a 50 mg/dL glucose drop than a user with a TDI of 100 U. This example assumes application of the 1800 rule of TDI/1800 (eg, 10/1800 or 1/180) to determine the insulin ratio required to produce a 1 mg/dL decrease in glucose. For a user with a TDI of 10 U, a 50 mg/dL drop would require 50/180 U or 0.28 U. On the other hand, for a user with a TDI of 100U, the ratio is 100/1800 or 1/18 and the amount of insulin required is 50/18 or 2.8U. Given this difference, it is necessary to scale the factors Q and R based on TDI or another metric of daily insulin need.

Therefore, the cost function should take into account the different daily insulin needs of the users. Example implementations may attempt to consider such various needs and provide appropriate scaling of the coefficients Q and R.

Exemplary embodiments may modify the coefficients Q and R to account for different daily insulin needs of the user. In one embodiment, the coefficient is<img file="JP7463533B2_D0004.tif" />It is calculated as

In these equations, Q_baseand R_baseis P_baseConstruct standard reference coefficients that would be suitable for users with general clinical parameter equations equivalent to . P is a custom value of the user's actual insulin needs.

The values of l and o may be set to have values that reflect the user's dependence on parameter variations. In some examples, l or o may have a value of zero such that the associated weights Q or R may not use scaled cost weighting factors.

Given the above formulation of the Q and R coefficients, the penalty function for the exemplary embodiment is:<img file="JP7463533B2_D0005.tif" />It may be expressed as

This Q and R factor formulation scales the glucose excursion and insulin excursion penalties. Glucose excursion is an example where the BG level changes from the target BG level, and insulin excursion is an example where the insulin dosage changes from the basal insulin dosage. The cost of glucose excursion increases with higher parameters (e.g.<img file="JP7463533B2_D0006.tif" />), while the cost of insulin excursion increases with lower parameters (e.g.<img file="JP7463533B2_D0007.tif" />) increases with Therefore, the cost is high for users with large insulin needs, which implies that higher amounts of insulin are required to bring a high glucose excursion back on target. Also, for users with small insulin needs, the cost of insulin excursion is high, implying that any insulin supply is an even larger fraction of the user's daily insulin needs.

FIG. 4 depicts a flowchart 400 summarizing the illustrative steps that may be taken to calculate the cost for each penalty function for a proposed insulin dose. The control device is<img file="JP7463533B2_D0008.tif" />(402). The control device is<img file="JP7463533B2_D0009.tif" />Calculate a glucose cost weighting factor, such as (404). The control device is<img file="JP7463533B2_D0010.tif" />(406). The control device is<img file="JP7463533B2_D0011.tif" />(408). Finally, the controller may complete the calculation of the cost for the insulin dose (410) per the above equation for the penalty function. This cost is determined for each proposed dose of insulin to identify the dosage with the best cost (eg, the lowest cost dosage).

Different formulations may be used for different example embodiments in determining glucose cost weighting factors. FIG. 5 provides a flowchart 500 of illustrative steps that may be performed to determine glucose cost weighting factors. First, a reference value for the parameter (e.g., P_base) is determined (502). If TDI is used, the value may be 120U. At that time, Q_baseThere are two options for calculating the ratio used to scale. In the above example case, the ratio of the custom value to the reference value is calculated (e.g.<img file="JP7463533B2_D0012.tif" />)(504). Conversely, an inverse ratio may be calculated instead. The inverse value is the ratio of the standard to the custom value<img file="JP7463533B2_D0013.tif" />(506) The value of the index (eg, l) may then be applied to the ratio (506).

Similarly, as displayed in the flowchart 600 of FIG. 6, the insulin cost weighting factor Q may be determined in different manners. First, a baseline insulin cost weighting factor (e.g., Q_base) is determined (602). The ratio used to scale the baseline insulin cost weighting factor may be determined as the ratio of the baseline parameter of daily insulin needs to the user's custom custom daily insulin needs (e.g.,<img file="JP7463533B2_D0014.tif" />)(604). Alternatively, an inverse ratio may be used (e.g.<img file="JP7463533B2_D0015.tif" />)(606). An index for the ratio may be assigned (608) as described above.

The effect of scaling the cost weighting factors can be seen in plots 700, 702, and 704 of FIG. These plots 700, 702 and 704 are for a user with a very high TDI of 115U. Plot 700 shows the user's BG level over time in a worst-case scenario using unscaled cost weighting factors, and plot 702 shows insulin delivery over time for such a case. The first portion 708 of the plot 700 shows that the graph 710 of the BG level is because the insulin supply did not increase by more than 0.2 U for an extended period of time until about 3:00 am, as shown by the graph 712 in the plot 702. , remaining above 250 mg/dL for an extended period of time starting near midnight and extending until about 5:00 am. This is a high weight product for insulin excursion.

In contrast, for a scaled cost weighting factor, the insulin dose would increase earlier (e.g., 0.3U starting at time 12:00am) and at an earlier time, as shown by graph 714 in plot 704. Stay elevated for a long time. This reduces elevated glucose levels more quickly. As mentioned above, for insulin resistant users, scaling can allow even larger doses of insulin to be delivered.

By scaling, the insulin excursion penalty decreases and the glucose excursion penalty increases. The graph 710 also shows that during the overnight period between 9:00pm and 12:00pm in the second portion 709 of the plot 702, as shown by regions 716 and 718 with unscaled cost weighting factors. is at risk of hypoglycemia. This is due to the relatively high insulin penalty compared to the user's input base of approximately 2U per hour. Therefore, it is difficult to vary large doses from the baseline without incurring many penalties.

In contrast, with scaled cost weighting factors, the insulin excursion penalty is not as large, so the amount of insulin can be varied further and avoid a prolonged period of hypoglycemia risk (7: starting around 30pm and by 9:00pm see the dose drop to 0U on graph 714).

FIG. 8 shows similar plots 800, 802, and 804 for an example with a user with a very low TDI of 15U. Plot 800 shows the user's BG level over time, as shown by graph 810 at 5 minute intervals. Regions 816 and 818 are shaded. As shown, the user's glucose level increases rapidly to over 200 mg/dL between 12:00am and 1:00am, and remains almost unchanged near 300 mg/dL. Therefore, the patient was hyperglycemic during this interval. A system without cost weighting factor scaling will continue to recommend a delivered dosage close to four times the constraint (eg, 4×basal) over an hour, as shown by graph 812 in plot 802. The user's BG levels plummet in response to the resulting hypoglycemia between 4:00am and 5:00am, as too much insulin was delivered without breaking the constraint. Glucose levels spike and overshoot the target when insulin delivery is interrupted in response to hypoglycemia.

In contrast, with a scaled cost weighting factor, the amount of insulin delivered decreases and decreases even faster as the user's glucose level begins to fall, as shown by graph 814 in plot 804. This is because the penalty for insulin excursion will be higher for such users, while the penalty for glucose excursion will be lower. Thus, overshoot and resulting hypoglycemia are avoided. This may also avoid hyperglycemia due to discontinuation of insulin in response to hypoglycemia.

The value of the cost weighting factor may be constrained to not exceed a low bound and/or a high bound. FIG. 9 shows a flowchart 900 of steps that may be performed using such boundaries. In one example case, the value of parameter P is calculated as follows.

<img file="JP7463533B2_D0016.tif" />

This is the cost function<img file="JP7463533B2_D0017.tif" />bring about.

Therefore, to realize the boundary, the user's insulin needs P_actualCustom parameters for are calculated (902). upper boundary P_highand the lower boundary P_lowis determined (904). Boundary P_highand P_lowThe minimum value is determined among P_actualis compared to determine the largest value (eg, the maximum value), and the largest value is used as the value P in the cost weighting factor ratio (906). The use of boundaries can help prevent the ratio from becoming too large or too small.

The cost function may be modified to account for user-specific values other than TDI. For example, the cost function may include basal, correction factors, or other clinical parameters such as insulin carb ratio. Scaling does not have to rely on a single parameter like TDI, but can instead rely on a combination of multiple parameters. Specifically, the variable represented by P in the previous expression generally defines the user's actual insulin needs. However, although a user's insulin needs may be generally defined by TDI, those needs may also be defined by basal parameters, correction factor parameters, or insulin carb ratio parameters.

For example, the general reference clinical parameter P_basecan be defined as the population-wide average TDI for a typical person with type 1 diabetes. On the other hand, this parameter can be defined as the average basal parameter of a typical person with type 1 diabetes.

In an alternative embodiment, P_baseis the following equation<img file="JP7463533B2_D0018.tif" />can be defined as various combinations of the average values of TDI and fundamental parameters, such as: W_TDIand W_basalrepresents the TDI and the weighting of the basic parameters to calculate the dependence of the cost function on each parameter. It is important to note that both the TDI and basal parameters have a direct relationship with the user's insulin needs, i.e. the higher the user's insulin needs, the higher their value, leading to this form of the equation. It is.

Accordingly, P_actualis similarly<img file="JP7463533B2_D0019.tif" />can be defined as

In further embodiments, a correction factor may be utilized in a manner similar to TDI and basis in the equation above. However, correction factors and similar parameters increase in value with decreasing insulin needs and vice versa, resulting in P_baseand P_actualcan be calculated using the following equation.

<img file="JP7463533B2_D0020.tif" />

A combination of all three parameters or other parameters can also be expressed as P_baseand P_actualcan be used to calculate. In one embodiment, these calculations can be formulated as the following equations.

<img file="JP7463533B2_D0021.tif" />

Other parameters with a positive relationship (i.e. an increase in insulin need results in an increase in the value of the clinical parameter) are P_baseand P_actualP_baseand P_actualmay be added to the denominator of the equation for .

Although the invention has been described herein with reference to exemplary embodiments, various changes in form and detail may depart from the intended scope of the invention as defined in the appended claims. It will be understood that this can be done without exception.

<u style="Single">[Configuration 1]</u><u style="Single">A device for controlling the supply of insulin from an artificial pancreas to a user, the device comprising:</u><u style="Single">a monitor interface with the glucose monitor for obtaining glucose measurements for the user from the glucose monitor;</u><u style="Single">an artificial pancreas interface for communicating with the artificial pancreas to control delivery of insulin to the user;</u><u style="Single">a processor configured to implement a control loop for controlling the supply of insulin by the artificial pancreas, the processor comprising:</u><u style="Single">the processor selects the insulin delivery dosage for the next delivery among the delivery dosage options that has the best cost function value;</u><u style="Single">The cost function is</u><u style="Single">a glucose cost factor reflecting a difference between a predicted glucose level and a target glucose level for the user to create the dosage option for the user;</u><u style="Single">an insulin cost factor that reflects how different the dosage option is from a current reference insulin dosage;</u><u style="Single">having a glucose cost weighting coefficient for weighting the glucose cost element;</u><u style="Single">a processor having an insulin cost weighting factor for weighting the insulin cost component;</u><u style="Single">The apparatus, wherein at least one of the glucose cost weighting factor and the insulin cost weighting factor has a value customized for the user.</u><u style="Single">[Configuration 2]</u><u style="Single">2. The apparatus of configuration 1, wherein the processor directs the artificial pancreas through the artificial pancreas interface to deliver the selected insulin delivery dosage.</u><u style="Single">[Configuration 3]</u><u style="Single">2. The apparatus of configuration 1, wherein only one of the glucose cost weighting factor and the insulin cost weighting factor has a customized value for the user.</u><u style="Single">[Configuration 4]</u><u style="Single">2. The apparatus of configuration 1, wherein both the glucose cost weighting factor and the insulin cost weighting factor have values customized for the user.</u><u style="Single">[Configuration 5]</u><u style="Single">2. The apparatus of configuration 1, wherein the glucose cost weighting factor has a value of a reference glucose cost weighting factor multiplied by a value indicating a ratio of a custom value representing insulin needs of the user to a reference value representing insulin needs.</u><u style="Single">[Configuration 6]</u><u style="Single">6. The apparatus according to configuration 5, wherein the value indicating the ratio is a value of an index of the ratio.</u><u style="Single">[Configuration 7]</u><u style="Single">2. The device of configuration 1, wherein the insulin cost weighting factor has a value of a reference insulin cost weighting factor multiplied by a value indicating a ratio of a reference value representing insulin needs to a custom value representing insulin needs of the user.</u><u style="Single">[Configuration 8]</u><u style="Single">8. The apparatus according to configuration 7, wherein the value indicating the ratio is a value of an index of the ratio.</u><u style="Single">[Configuration 9]</u><u style="Single">2. The apparatus of configuration 1, wherein the glucose cost weighting factor has a value of a reference glucose cost weighting factor multiplied by a value indicating a ratio of a reference value representing insulin needs to a custom value representing insulin needs of the user.</u><u style="Single">[Configuration 10]</u><u style="Single">2. The device of configuration 1, wherein the insulin cost weighting factor has a value of a reference insulin cost weighting factor multiplied by a value indicating a ratio of a custom value representing the user's insulin needs to a reference value representing insulin needs.</u><u style="Single">[Configuration 11]</u><u style="Single">2. The device according to configuration 1, wherein the artificial pancreas interface is a wireless communication interface.</u><u style="Single">[Configuration 12]</u><u style="Single">The device of configuration 1, wherein the device is one of a mobile computing device, a smartphone, or an insulin pump component.</u><u style="Single">[Configuration 13]</u><u style="Single">2. The apparatus of configuration 1, wherein the processor enforces boundaries on parameters used in determining at least one of the glucose cost weighting factor or the insulin cost weighting factor customized for the user.</u><u style="Single">[Configuration 14]</u><u style="Single">14. The apparatus of configuration 13, wherein the parameter is a value indicative of the insulin needs of the user.</u><u style="Single">[Configuration 15]</u><u style="Single">The processor determines at least one of the glucose cost weighting factor or the insulin cost weighting factor based on at least one of a correction factor for insulin sensitivity for the user, an insulin carb ratio for the user, or a basal insulin level for the user. The apparatus according to configuration 1, configured to determine one.</u><u style="Single">[Configuration 16]</u><u style="Single">The device according to configuration 1, wherein at least one of the glucose cost weighting factor and the insulin cost weighting factor has a value customized for a TDI (Total Daily Insulin) user.</u><u style="Single">[Configuration 17]</u><u style="Single">receiving a glucose measurement for the user from the glucose monitor;</u><u style="Single">determining a dosage for a subsequent supply of insulin from the artificial pancreas to the user, the step comprising:</u><u style="Single">The step of determining includes:</u><u style="Single">applying a cost function to multiple possible doses of insulin to the user;</u><u style="Single">selecting one of the possible dosages of insulin that has the best cost under the cost function;</u><u style="Single">The cost function is</u><u style="Single">a glucose cost factor reflecting a difference between a predicted glucose level and a target glucose level for the user to create the dosage option for the user;</u><u style="Single">an insulin cost factor that reflects how different the dosage option is from a current reference insulin dosage;</u><u style="Single">having a glucose cost weighting coefficient for weighting the glucose cost element;</u><u style="Single">an insulin cost weighting factor for weighting the insulin cost element;</u><u style="Single">the glucose cost weighting factor and the insulin cost weighting factor have values customized for the user;</u><u style="Single">directing the artificial pancreas to deliver the selected dosage to the user.</u><u style="Single">[Configuration 18]</u><u style="Single">A non-transitory computer-readable storage device storing computer-readable instructions, the device comprising:</u><u style="Single">The computer readable instructions cause a processor of the device to:</u><u style="Single">receiving glucose measurements for the user from the glucose monitor;</u><u style="Single">determining a dosage for a subsequent delivery of insulin from an artificial pancreas to the user, comprising:</u><u style="Single">Said determining:</u><u style="Single">applying a cost function to multiple possible doses of insulin to the user;</u><u style="Single">selecting one of the possible dosages of insulin that has the best cost under the cost function;</u><u style="Single">The cost function is</u><u style="Single">a glucose cost factor reflecting a difference between a predicted glucose level and a target glucose level for the user to create the dosage option for the user;</u><u style="Single">an insulin cost factor that reflects how different the dosage option is from a current reference insulin dosage;</u><u style="Single">having a glucose cost weighting coefficient for weighting the glucose cost element;</u><u style="Single">an insulin cost weighting factor for weighting the insulin cost element;</u><u style="Single">the glucose cost weighting factor and the insulin cost weighting factor have values customized for the user;</u><u style="Single">and instructing the artificial pancreas to deliver the selected dosage to the user.</u><u style="Single">[Configuration 19]</u><u style="Single">19. The non-transitory computer-readable storage medium of configuration 18, wherein only one of the glucose cost weighting factor and the insulin cost weighting factor has a customized value for the user.</u><u style="Single">[Configuration 20]</u><u style="Single">19. The non-transitory computer-readable storage medium of configuration 18, wherein both the glucose cost weighting factor and the insulin cost weighting factor have values customized for the user.</u><u style="Single">[Configuration 21]</u><u style="Single">19. The non-temporary glucose cost weighting factor of configuration 18, wherein the glucose cost weighting factor has a value of a reference glucose cost weighting factor multiplied by a value indicating a ratio of a custom value representing insulin needs of the user to a reference value representing insulin needs. Computer-readable storage medium.</u><u style="Single">[Configuration 22]</u><u style="Single">22. The non-transitory computer-readable storage medium of claim 21, wherein the value indicative of the ratio is a value of an exponent of the ratio.</u><u style="Single">[Configuration 23]</u><u style="Single">19. The non-temporary insulin cost weighting factor of configuration 18, wherein the insulin cost weighting factor has a value of a reference insulin cost weighting factor multiplied by a value indicating a ratio of a reference value representing insulin needs to a custom value representing insulin needs of the user. Computer-readable storage medium.</u><u style="Single">[Configuration 24]</u><u style="Single">24. The non-transitory computer-readable storage medium of claim 23, wherein the value indicative of the ratio is a value of an exponent of the ratio.</u><u style="Single">[Configuration 25]</u><u style="Single">19. The non-temporary glucose cost weighting factor of configuration 18, wherein the glucose cost weighting factor has a value of a baseline insulin cost weighting factor multiplied by a value indicating a ratio of a reference value representing insulin needs to a custom value representing insulin needs of the user. Computer-readable storage medium.</u><u style="Single">[Configuration 26]</u><u style="Single">19. The non-temporary glucose cost weighting factor of configuration 18, wherein the glucose cost weighting factor has a value of a baseline insulin cost weighting factor multiplied by a value indicating a ratio of a reference value representing insulin needs to a custom value representing insulin needs of the user. Computer-readable storage medium.</u><u style="Single">[Configuration 27]</u><u style="Single">19. The non-transitory of configuration 18, further storing instructions for enforcing boundaries on parameters used in determining at least one of the glucose cost weighting factor or the insulin cost weighting factor customized for the user. computer-readable storage medium.</u><u style="Single">[Configuration 28]</u><u style="Single">28. The non-transitory computer-readable storage medium of claim 27, wherein the parameter is a value indicative of the insulin needs of the user.</u><u style="Single">[Configuration 29]</u><u style="Single">Determining at least one of the glucose cost weighting factor or the insulin cost weighting factor comprises determining at least one of a correction factor for insulin sensitivity for the user, an insulin carb ratio for the user, or a basal insulin level for the user. 19. The non-transitory computer-readable storage medium according to configuration 18, based on</u><u style="Single">[Configuration 30]</u><u style="Single">19. The non-transitory computer-readable storage medium of arrangement 18, wherein at least one of the glucose cost weighting factor and the insulin cost weighting factor has a value customized for a Total Daily Insulin (TDI) user.</u>