Abstract: Exemplary embodiments may perform data analytics on data regarding a user of a medicament delivery device and data regarding other users of like medicament delivery devices to provide insights and guide management of the medicament delivery device for the user. The data analytics may be so called “big data” analytics that are performed on large data sets. The data analytics may be performed on various types of data to help determine what actions, if any, the remote management should take. The data analytics may be performed by the software on the remote management device or by other computational resources, such as by other servers or cloud computing resources. Exemplary embodiments may provide for remote management of a medicament delivery device. The management may be remote in that the management is performed via a remote management device, such as a computing device, that is remotely located from the medicament delivery device.

Abstract: Methods and apparatuses for performing a blood glucose monitoring process are described. For example, an apparatus may include at least one memory and logic coupled to the at least one memory. The logic may operate to determine patient monitoring information associated with a diabetic treatment of a patient, generate at least one monitoring information structure based on the patient monitoring information, generate at least one monitoring information image based on at least a portion of the at least one monitoring information structure, and process the at least one monitoring information image using a computational model to determine a blood glucose condition of the patient. Other embodiments are described.

Abstract: Techniques and devices for determining and generating insulin formulations are described.

Abstract: Disclosed are systems, devices, methods and computer-readable medium products that compensate for consumption of a meal by providing a reduced-constraint meal bolus dosage. The reduced-constraint meal bolus dosage is determined using post-prandial safety constraints that are reduced based on an indication that a meal has been consumed. The post-prandial safety constraints are intended to protect users from overdelivering or underdelivering insulin to the user. The determinations may be performed by a control algorithm-based drug delivery system that enables automatic delivery of a drug, such as insulin or the like.

Abstract: Disclosed are techniques to establish a modified pump rate that mitigates the effects of a pump occlusion and enables a recommended dosage of insulin to be output by a pump mechanism over the course of a control cycle. In an example, the pump rate may be reduced by adding a calculated time interval between application of actuation commands to extend the amount of time over which insulin may be output by the pump mechanism.

Abstract: Disclosed are techniques to establish initial settings for an automatic insulin delivery device. An adjusted total daily insulin (TDI) factor usable to calculate a TDI dosage may be determined. The adjusted TDI factor may be a TDI per unit of a physical characteristic of the user (e.g., weight) times a reduction factor. The adjusted TDI factor may be compared to a maximum algorithm delivery threshold. Based on the comparison result, the application or algorithm may set a TDI dosage and output a control signal. Blood glucose measurement values may be collected from a sensor over a period of time. A level of glycated hemoglobin of the blood may be determined based on the obtained blood glucose measurement values. In response to the level of glycated hemoglobin, the set TDI dosage may be modified. A subsequent control signal including the modified TDI dosage may be output to actuate delivery of insulin.

Abstract: Exemplary embodiments may address the problem of missing blood glucose concentration readings from a glucose monitor that transmits blood glucose concentration readings over a wireless connection due to problems with the wireless connection. In the exemplary embodiments, an automated insulin delivery (AID) device uses an estimate in place of a missing blood glucose concentration reading in determining a predicted future blood glucose concentration reading for a user. Thus, the AID device is able to operate normally in generating insulin delivery settings despite not receiving a current blood glucose concentration reading for a current cycle. There is no need to suspend delivery of insulin to the user due to the missing blood glucose concentration reading.

Abstract: Exemplary embodiments described herein relate to a closed loop artificial pancreas system. The artificial pancreas system seeks to automatically and continuously control the blood glucose level of a user by emulating the endocrine functionality of a healthy pancreas. The artificial pancreas system uses a closed loop control system with a cost function. The penalty function helps to bound the infusion rate of insulin to attempt to avoid hypoglycemia and hyperglycemia. However, unlike conventional systems that use a generic or baseline parameter for a user's insulin needs in a cost function, the exemplary embodiments may use a customized parameter in the cost function that reflects the individualized insulin needs of the user. The use of the customized parameter causes the cost function to result in insulin dosages over time better suited to the individualized insulin needs of the user. This helps to better avoid hypoglycemia and hyperglycemia.

Abstract: The exemplary embodiments attempt to identify impending hypoglycemia and/or hyperglycemia and take measures to prevent the hypoglycemia or hyperglycemia. Exemplary embodiments may provide a drug delivery system for delivering insulin and glucagon as needed by a user of the drug delivery system. The drug delivery system may deploy a control system that controls the automated delivery of insulin and glucagon to a patient by the drug delivery system. The control system seeks among other goals to avoid the user experiencing hypoglycemia or hyperglycemia. The control system may employ a clinical decision support algorithm as is described below to control delivery of insulin and glucagon to reduce the risk of hypoglycemia or hyperglycemia and to provide alerts to the user when needed. The control system assesses whether the drug delivery system can respond enough to avoid hypoglycemia or hyperglycemia and generates alerts when manual action is needed to avoid hypoglycemia or hyperglycemia.

Abstract: In an automated insulin delivery device, fuzzy logic may be applied to the responding to the possibility of a user taking additional action that may affect the blood glucose concentration. Fuzzy sets may be defined for empirically derived different likelihoods of the user taking such additional action based on correlated factors. A membership function may be provided for each fuzzy set. The membership function may provide a probability of membership in the set based on a parameter. Each fuzzy set may have a response that is reflective of the likelihood of additional user action associated with the fuzzy set. The responses by the AID device to each of these cases may reflect the probability of each such case occurring as evidenced by empirical data.

Abstract: Exemplary embodiments may provide an improved approach to automated insulin delivery by more accurately estimating the total daily insulin (TDI) of a user. As a result, less insulin is wasted by the delivery system, and the estimate of TDI more closely matches a user's actual daily insulin needs. Hence, the user need not refill the insulin reservoir excessively or need not fret unnecessarily about running out of insulin prematurely. The estimate relies on the history of actual automated insulin deliveries and thus reflects the actual insulin delivered to the user more accurately than conventional approaches.

Abstract: Disclosed are a device, system, methods and computer-readable medium products that provide techniques to implement functionality to receive information related to a meal and delivery of a meal bolus. The received information may be stored and utilized in the generation of insulin delivery profiles. An estimate a mealtime insulin need based on the generated insulin delivery profiles may be generated. A dose of insulin may be determined that satisfies an estimated mealtime insulin need based on the generated insulin delivery profiles. The determined dose of insulin may be output at a time corresponding to a mealtime.

Abstract: Exemplary embodiments provide an approach to predicting meal and/or exercise events for an insulin delivery system that otherwise does not otherwise identify such events. The insulin delivery system may use a model of glucose insulin interactions that projects estimated future glucose values based on the history of glucose values and insulin deliveries for the user. The predictions of meal events and/or exercise events may be based on residuals between actual glucose values and predicted glucose values. The exemplary embodiments may calculate a rate of change of the residuals over a period of time and compare the rate of change to thresholds to determine whether there likely has been a meal event or an exercise event. The insulin delivery system may then take measures to account for the meal or exercise by the user.

Abstract: Disclosed are a device, system, methods and computer-readable medium products that provide techniques to implement functionality to receive blood glucose measurements over a period of time. An average of missing blood glucose measurement values may be maintained over a predetermined time period. A count of a number of missing blood glucose measurement values over a period of time may be established. A controller may calculate a divergence of the number of missing blood glucose measurement values over the period of time from the average of missing blood glucose measurements over the predetermined time period. Based on a value of the divergence, a determination that a safety constraint for delivery of insulin is to be reduced. The safety constraint may be reduced by a predetermined percentage. An instruction to deliver an insulin dosage may be generated according to the reduced safety constraint may be forwarded to a wearable drug delivery device.

Abstract: Disclosed are devices, a system, methods and computer-readable medium products that provide techniques to implement functionality to determine when to softened upper bounds of drug delivery and how much to soften the upper bound. Future blood glucose measurement values may be predicted by calculating deviations between predicted blood glucose measurement values and additional blood glucose measurement values. A gain parameter may be determined for use with a model of predicting a user's blood glucose measurement values and determining future drug dosages. Safety constraints may be determined based on an evaluation of missing blood glucose measurement values. In addition, safety constraints may be determined based on an evaluation of a user's increased interaction with an automatic drug delivery device. A bolus uncertainty metric to determine an amount of insulin to be provided in a bolus dosage in response to a bolus request may be calculated.

Abstract: Disclosed are a device, system, methods and computer-readable medium products that provide an updated insulin-to-carbohydrate ratio and an updated total daily insulin. The described processes may be used for periodic updating of the insulin-to-carbohydrate ratio and the total daily insulin. The insulin-to-carbohydrate ratio and/or the total may be used in the calculation of new doses of insulin that a drug delivery device may be commanded to deliver to a user.

Abstract: A wearable drug delivery device and method for optimizing performance thereof are provided. A system may include a processor operable with memory, and a drug delivery device and sensor coupled to a user, the sensor operable to detect characteristics of the delivery device. A receiver operable on the processor receives an input signal from the sensor, the input signal representing the detected characteristics. A controller operable on the processor receives the input signal from the receiver, and retrieves, from memory, a baseline characteristics. The controller may determine a location of the delivery device and a tissue profile of the injection location based on a comparison between the detected characteristics and the baseline characteristics. The controller may further control or modify delivery of a liquid drug from the delivery device in response to the location of the delivery device.

Abstract: Disclosed are a device, a computer-readable medium, and techniques that provide an onboarding process and an adaptivity process for a drug delivery device. A processor executing an onboarding process determines whether a history of delivered insulin to a user meets certain sufficiency requirements. The onboarding process enables a processor to cause the drug delivery device to administer doses of insulin to a user according to an initial total daily insulin dose calculation that is determined based on the sufficiency of the insulin delivery history. The initial total daily insulin may be adapted according to the adaptivity process as new insulin delivery is collected. The insulin delivery history, when sufficient, may be used to set total daily insulin dosages that enable automated insulin delivery upon replacement of a drug delivery device. The adaptivity process may be implemented to modify an initial insulin delivery doses to provide adapted insulin delivery doses.

Abstract: Disclosed are a system, methods and computer-readable medium products that provide bolus dosage calculations by a control algorithm-based drug delivery system that provides automatic delivery of a drug, such as insulin or the like, based on sensor input. Blood glucose measurement values may be received at regular time intervals from a sensor. Using the blood glucose measurements, the control algorithm may perform various calculations and determinations to provide an appropriate bolus dosage. The appropriate bolus dosage may be used to respond to a trend in a trajectory of blood glucose measurements. In addition, a bolus dosage may also be determined by the disclosed device, system, method and/or computer-readable medium product in response to an indication that a user consumed a meal.

Abstract: A wearable drug delivery device, techniques, and computer-readable media that provide an application that implements a diabetes treatment plan for a user are described. The drug delivery device may include a controller operable to direct operation of the wearable drug delivery device. The controller may provide a selectable activity mode of operation for the user. Operation of the drug delivery device in the activity mode of operation may reduce a likelihood of hypoglycemia during times of increased insulin sensitivity for the user and may reduce a likelihood of hyperglycemia during times of increased insulin requirements for the user. The activity mode of operation may be manually activated by the user or may be activated automatically by the controller. The controller may automatically activate the activity mode of operation based on a detected activity level of the user and/or a detected location of the user.