University of California

Harvard College

Joslin Diabetes Center, Inc.

Sansum Diabetes Research Institute

Eyal Dassau

Francis J Doyle, Iii

Francis J Doyle

Ravi L Gondhalekar

Howard Zisser

Joon Bok Lee

Dale E Seborg

Rebecca Harvey

Benyamin Grosman

Alejandro J Laguna Sanz

Lois Jovanovic

Dawei Shi

Mary-Elizabeth Rueckel Patti

Howard Zissser

Lois Javanovic

Ankush Chakrabarty

Stamatina Zavitsanou

Francis J Doyle Iii

Francis J Iii Doyle

Iii Francis J Doyle

The present disclosure provides a hypoglycemia prediction algorithm (HPA) specifically designed for the unique postprandial glycemic patterns characteristic of PBH. This algorithm can predict impending hypoglycemia by performing a series of steps. The steps can include collecting data from at least one sensor. The data can comprise a concentration of glucose in the bloodstream of a subject. The data can be processed using the HPA and impending glucose concentrations can be calculated. The method can then provide for determining whether the predicted glucose concentrations are lower than a hypoglycemic threshold parameter. In response to determining that the predicted glucose concentrations are lower than the hypoglycemic threshold parameter, the method can provide for enacting an impending hypoglycemia protocol.

Prevention of post-bariatric hypoglycemia using a novel glucose prediction algorithm and mini-dose stable glucagon

A system for the delivery of insulin to a patient is provided. The systems and methods disclose include an insulin delivery device configured to deliver insulin to a patient in response to control signals. The system also includes a controller programmed to receive the sensor glucose measurement signal from the glucose sensor. The sensor glucose measurement signal received indicates a concentration of the real time glucose concentration in a bloodstream. The controller is further configured to enact an impeding glycemia protocol based on a zone model predictive control (MPC) algorithm in response to the real time glucose concentration. The impeding glycemia protocol includes in determining a relationship between predicted glucose concentrations, a rate of change of the predicted glucose concentrations, and a set of control parameters that determine insulin doses above and below a patient-specific basal rate.

Adaptive zone model predictive control with a glucose and velocity dependent dynamic cost function for an artificial pancreas

A model-based control scheme consisting of either a proportional-integral-derivative (IMC-PID) controller or a model predictive controller (MPC), with an insulin feedback (IFB) scheme personalized based on a priori subject characteristics and comprising a lower order control-relevant model to obtain PID or MPC controller for artificial pancreas (AP) applications.

Model-based personalization scheme of an artificial pancreas for type I diabetes applications

The development of artificial pancreas (AP) technology for deployment in low-energy, embedded devices is contingent upon selecting an efficient control algorithm for regulating glucose in people with type 1 diabetes mellitus (T1DM). The energy consumption of the AP can be lowered by reducing updates of the control model: the number of times the decisionmaking algorithm is invoked to compute an appropriate insulin dose.

Event-triggered model predictive control for embedded artificial pancreas systems

A state-estimator for the estimation or initialization of the state of a discrete-time state-space dynamical model based on sensor measurements of the model output, comprising fitting a continuous-time function to acquired sensor measurement data-points of each model output, and subsequently sampling the continuous time function at exactly the sample-period of the state-space dynamic model for which the state is being estimated or initialized, in order to construct a model state via a synthesized output trajectory.

Moving-horizon state-initializer for control applications

Presented herein are methods, and devices of adaptively tuning a zone based Model Predictive Control (MPC) controller, using at least one processor, which include determining, residuals based on prediction models storing, in a memory, the determined residuals, calculating a trust index by quantifying uncertainty of the prediction models using the stored residuals and tuning the MPC controller, in real time based on the calculated value of the trust index.

Predictive control model for the artificial pancreas using past predictions

Methods, devices, algorithms, and systems controlling insulin delivery employ velocity-weighting. Predicted glucose outcomes are penalized with a cost modulated by a factor that is a function of the glucose velocity, wherein glucose outcomes are penalized increasingly less for increasingly negative glucose velocities, when glucose level is high, and/or wherein a hyperglycemic glucose value that is already converging to the euglycemic zone results in less corrective action by the controller than were the hyperglycemic state steady.

Velocity-weighting model predictive control of an artificial pancreas for type 1 diabetes applications

A controller for an artificial pancreas for automated insulin delivery to patients with type 1 diabetes mellitus (T1DM) that enforces safe insulin delivery throughout both day and night, wherein the controller employs zone model predictive control, whereby real-time optimization, based on a model of a human's insulin response, is utilized to regulate blood glucose levels to a safe zone, and time-dependent zones that smoothly modulate the controller correction based on the time of day, wherein the controller strategically strives to maintain an 80-140 mg/dL glucose zone during the day, a 110-220 mg/dL zone at night, and a smooth transition of 2 hour duration in between.

Daily periodic target-zone modulation in the model predictive control problem for artificial pancreas for type I diabetes applications

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.

Glucose rate increase detector: a meal detection module for the health monitoring system

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.

Health monitoring system

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

Maintaining multiple defined physiological zones using model predictive control

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.

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Goleta, CA, United States of America

Eyal Dassau