Abstract: Introduced here are diabetes management platforms able to guide people with diabetes toward a glycemic target. Rather than state the absolute amount of glucose within the blood, the diabetes management platform can instead produce personalized glycemic goals based on the physiological data associated with an individual. For example, if the diabetes management platform determines that the time spent within a first glycemic range exceeds a specified threshold, then the diabetes management platform may recommend that the individual attempt to keep their blood glucose level within a second glycemic range. Generally, the second glycemic range is closer than the first glycemic range to a target glycemic range corresponding to a healthy glycemic state.

Abstract: To better understand the impact that meals have on the blood glucose level, some individuals with diabetes have begun recording meals consumed over time. However, the relationship between meals consumed by an individual and the glycemic health state of the individual has been difficult to understand. Introduced here are computer programs and associated computer-implemented techniques for utilizing information regarding the meals consumed by an individual to manage the blood glucose level of the individual in a personalized manner. By consistently and properly attributing physiological responses represented as excursions in blood glucose measurements to meals, the relationship between these meals and the glycemic health state can be better understood. For example, by examining this relationship, a diabetes management platform may be able to identify appropriate recommendations for monitoring, managing, and/or improving the glycemic health state of an individual.

Abstract: Introduced here are diabetes management platforms able to guide people with diabetes toward a glycemic target. Rather than state the absolute amount of glucose within the blood, the diabetes management platform can instead produce personalized glycemic goals based on the physiological data associated with an individual. For example, if the diabetes management platform determines that the time spent within a first glycemic range exceeds a specified threshold, then the diabetes management platform may recommend that the individual attempt to keep their blood glucose level within a second glycemic range. Generally, the second glycemic range is closer than the first glycemic range to a target glycemic range corresponding to a healthy glycemic state.

Abstract: A glucose rate increase detector (GRID) for use in an artificial pancreas (AP), wherein the GRID detects in a person persistent increases in glucose associated with a meal, and either triggers a meal bolus to blunt meal peak safely, during closed-loop control, or alerts the person to bolus for a meal, during open-loop control.

Abstract: Introduced here are diabetes management platforms able to guide people with diabetes toward a glycemic target. Rather than state the absolute amount of glucose within the blood, the diabetes management platform can instead produce personalized glycemic goals based on the physiological data associated with an individual. For example, if the diabetes management platform determines that the time spent within a first glycemic range exceeds a specified threshold, then the diabetes management platform may recommend that the individual attempt to keep their blood glucose level within a second glycemic range. Generally, the second glycemic range is closer than the first glycemic range to a target glycemic range corresponding to a healthy glycemic state.

Abstract: The disclosure relates to drug delivery and maintaining multiple defined physiological zones using model predictive control.

Abstract: A glucose rate increase detector (GRID) for use in an artificial pancreas (AP), wherein the GRID detects in a person persistent increases in glucose associated with a meal, and either triggers a meal bolus to blunt meal peak safely, during closed-loop control, or alerts the person to bolus for a meal, during open-loop control.

Abstract: A method, computer implemented method and associated apparatus for the management of diabetes comprises utilizing zone model predictive control (Zone-MPC) to control delivery of an insulin or insulin analog within a zone of desired values.

Abstract: The disclosure relates to drug delivery and maintaining multiple defined physiological zones using model predictive control.

Abstract: A machine for processing continuous glucose monitoring data and issuing an alert if hypoglycemia is imminent has three modules: (a) a pre-processing module that receives and modulates continuous glucose monitoring data by reducing noise and adjusting for missed data points and shifts due to calibration; (b) a core algorithm module that receives data from the pre-processing module and calculates a rate of change to make a hypoglycemia prediction, and determine if hypoglycemia is imminent; and (c) an alarm mode module that receives data from the core algorithm and issues an audio or visual alert or warning message or a negative feedback signal to an insulin delivery device if hypoglycemia is imminent.

Abstract: A method, computer implemented method and associated apparatus for the management of diabetes is provided.