Abstract: Disclosed are techniques, devices and systems that obtain a glucose measurement history and a liquid drug delivery history. An expected drug delivery amount may be calculated based on the obtained glucose measurement history and the obtained liquid drug delivery history. A processor may calculate a plurality of respective drug delivery amounts implemented using different advisory mode algorithms. A respective advisory drug delivery amount of the plurality of respective advisory drug delivery amounts may be selected by the processor. A recommendation may be generated based on the selected respective advisory drug delivery amount.

Abstract: The exemplary embodiments may dynamically adjust an Insulin on Board (JOB) profile for a user of a medicament delivery system to customize the IOB profile to the user based on recent glucose level history and insulin deliveries rather than use the conventional static IOB profiles that are based on population averages. The exemplary embodiments may calculate a Duration of Insulin Action (DIA) for a user from the customized insulin decay curves. The exemplary embodiments may generate and send notifications to the user to help reduce the DIA of the user and/or to keep the DIA of the user from rising. The exemplary embodiments may aim to not overly constrain insulin delivery due to the contribution of insulin boluses to JOB.

Abstract: The exemplary embodiments may employ a glucose prediction model (GPM) that is tailored to a user to account for insulin sensitivity or insulin insensitivity. The exemplary embodiments may predict future glucose levels based on past glucose levels for the user. Specifically, the GPM in exemplary embodiments may predict the future glucose level of the user as a weighted sum of most recent glucose level readings from the user. The exemplary embodiments may employ linear regression analysis to determine the values of the weights. These weights customize the GPM of the user based on the user's most recent glucose level history. Due to the customization, the GPM may more accurately predict future glucose levels of the user. As a result, the AID may exhibit better glucose level control for the user. The GPM of the exemplary embodiments may be updated on an ongoing basis.

Abstract: Disclosed are techniques and devices that are operable to receive one or a number of generalized parameters of an automated insulin delivery algorithm. An input of at least one generalized parameter corresponding to a user may be used to set one or more of the number of specific parameters of the automated insulin delivery algorithm based on the inputted at least one generalized parameter. Physiological condition data related to the user may be collected. The automated insulin delivery algorithm may determine a dosage of insulin to be delivered based on the collected physiological condition. Signals may be output to cause a liquid drug to be delivered to the user based on an output of the automated insulin delivery algorithm related to the determined dosage of insulin.

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: Disclosed herein are various embodiments comprising methods to propose or modify a basal profile of a user, wherein the basal profile is delivered by an automatic drug delivery system operating in manual mode or is administered manually by the user. Suggestions of initial basal profiles or modifications to existing basal profiles may be based on previous glucose control outcomes or previous insulin delivery as recorded by the automatic drug delivery system.

Abstract: Disclosed herein is a drug delivery system wherein wearable components of the system are provided with Wi-Fi and/or cellular capabilities such as to allow different modalities for inter-component communications and, further, to allow access to a plethora of cloud-based services to enhance the functionality of the system.

Abstract: Disclosed are techniques, processes, systems, and programming code that enables users of automatic drug delivery devices to initiate the automatic drug delivery device for an initial use phase as well as implement changes that control the adjustment of target setpoints by a medication delivery algorithm used in the control of the automatic drug delivery device. During the initial use phase, the automatic drug delivery device may be based on the user's glucose control outcome without performing time consuming and frequent settings of sensor values as well as allowing users to remain substantially within range of any target setpoints. In addition, the disclosed techniques, processes, systems, and programming code enable users to have initial dosage settings automatically set without having to input what were previously considered fundamental inputs.

Abstract: The exemplary embodiments provide an automated approach for adjusting the medicament delivery rate to the user when operating in an open loop manner (“open mode”). The approach relies upon an insulin delivery history to the user make adjustments to the medicament delivery rate in the open mode. In particular, the exemplary embodiments may look at the medicament delivery history while the medicament delivery device is operating in a closed loop manner (“closed mode”) to determine how to adjust the open mode medicament delivery rate. It is presumed that in closed mode, the control system of the medicament device has gained knowledge over time about how to control the medicament delivery rate to produce good treatment outcomes for the user. The exemplary embodiments leverage this knowledge to adjust the open mode medicament delivery rates. Medicament bolus deliveries in open mode may also be adjusted in like fashion.

Abstract: The exemplary embodiments may provide adaptivity in the basal portion value. The exemplary embodiments may also provide adaptivity of the total daily medicament (TDM) delivery amount. The adaptivity provided by the exemplary embodiments helps to customize medicament delivery to the needs of the user of a medicament delivery device. The exemplary embodiments may adapt the basal ratio for the user based on historical medicament delivery data for the user. In addition, the exemplary embodiments may update the basal ratio based upon more recent trends that diverge from the historical medicament delivery data. The degree of adaptivity provided by the exemplary embodiments may be bound to not exceed an upper threshold and/or a lower threshold. The degree of adaptivity may be modulated by controlling the rate of adaptivity too small increments each time the basal ratio is updated.

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 and devices that are operable to receive one or a number of generalized parameters of an automated insulin delivery algorithm. An input of at least one generalized parameter corresponding to a user may be used to set one or more of the number of specific parameters of the automated insulin delivery algorithm based on the inputted at least one generalized parameter. Physiological condition data related to the user may be collected. The automated insulin delivery algorithm may determine a dosage of insulin to be delivered based on the collected physiological condition. Signals may be output to cause a liquid drug to be delivered to the user based on an output of the automated insulin delivery algorithm related to the determined dosage of insulin.

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: Disclosed are processes and techniques for a correction factor determination process for determining a correction factor for delivering a drug to a patient via a drug delivery device. For example, for an insulin delivery device, the disclosed techniques enable adjustment of the patient’s correction factor in real time based on the deviation of the patient’s glucose concentrations against clinically recommended targets. For example, a method for determining a correction factor may include determining an insulin action (Iaction) for a patient over a duration, the insulin action representing a total insulin metabolized, determining glucose information for the patient over the duration, the glucose information representing glucose activity, and determining an estimated correction factor (CFest) based on (glucose information) / (insulin action) for the duration. Other embodiments are described.

Abstract: Disclosed are a computing apparatus, a computer-readable medium and a method that enable a user to use subjective inputs to alter settings of a wearable automatic drug delivery system. A user input device is presented on a graphical user interface that enables input of a subjective insulin need parameter. In response to receiving the input, the processor may modify a subjective coefficient value. A specific factor useable by an automatic insulin delivery algorithm is set based on the subjective coefficient value. Physiological condition data related to the user and the set specific factor may be used to determine a dosage of insulin to be delivered to the user based on the collected physiological condition data of the user. The processor may cause the determined dosage of insulin to be delivered to the user based on an output of the automatic insulin delivery algorithm.

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.

Abstract: Exemplary embodiments may provide a user interface and logic for assisting a user in calculating a proper medicament bolus dosage. The user interface may be simple and easy to understand. The user interface may clearly specify needed inputs, such as a carbohydrates amount for a meal that is about to be ingested and the current glucose level reading for the user. Entered inputs may be displayed on the user interface. The user interface may depict key calculated values resulting from calculations that involve the inputs, including the total bolus dosage calculation. The user interface also may depict the values that contribute to the total bolus dosage calculation. Some of the input values, such as carbohydrates amount, may be prepopulated by the management device or medicament delivery device. The user interface may permit overriding of prepopulated input values.

Abstract: Disclosed herein are systems and methods for providing safeguards, in an automatic drug delivery system, to prevent or compensate for insufficient delivery of bolus doses of a liquid drug. The safeguards include, for example, features built into the medication delivery algorithm to allow the medication delivery algorithm to ensure that proper bolus doses are delivered, either manually by the user or automatically by the drug delivery system. In addition, various methods are described for providing the medication delivery algorithm with a means for determining when a meal has been ingested and, in some embodiments, for providing an automatic bolus dose of the liquid drug in response to the determination.

Abstract: Disclosed are an audio processing method performed by an electronic device, a non-transitory computer-readable storage medium, and a computer program product. The method includes: sampling multiple fragments of audio data of a target object to obtain reference audio data of the target object; performing audio encoding on the reference audio data of the target object to obtain a reference embedding vector of the reference audio data; performing tone-based attention processing on the reference embedding vector of the reference audio data to obtain a tone embedding vector of the target object, wherein the tone embedding vector is independent from content of the audio data; and generating audio data of a target text that conforms to a tone of the target object according to the tone embedding vector of the target object.

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.