公开(公告)号：US10856786B2

公开(公告)日：2020-12-08

申请号：US16154997

申请日：2018-10-09

Applicant: MEDTRONIC MINIMED, INC.

Inventor： Garry M. Steil ,  Sami S. Kanderian, Jr. ,  Martin T. Cantwell ,  Udo Hoss

IPC: A61B5/145 ,  A61B5/1486 ,  A61B5/00 ,  A61M5/142 ,  A61M5/172 ,  A61K38/28 ,  G16H20/17 ,  A61B5/1495 ,  A61M5/158 ,  G16H20/10 ,  G06F19/00

Abstract: A system and method for controlling and monitoring a diabetes-management system through the use of a model that predicts or estimates future dynamic states of glucose and insulin from variables such as insulin delivery or exogenous glucose appearance as well as inherent physiological parameters. The model predictive estimator can be used as an insulin bolus advisor to give an apriori estimate of postprandial glucose for a given insulin delivery profile administered with a known meal to optimize insulin delivery; as a supervisor to monitor the operation of the diabetes-management system; and as a model predictive controller to optimize the automated delivery of insulin into a user's body to achieve a desired blood glucose profile or concentration. Open loop, closed-loop, and semi-closed loop embodiments of the invention utilize a mathematical metabolic model that includes a Minimal Model, a Pump Delivery to Plasma Insulin Model, and a Meal Appearance Rate Model.

公开(公告)号：US11918349B2

公开(公告)日：2024-03-05

申请号：US17084474

申请日：2020-10-29

Applicant: MEDTRONIC MINIMED, INC.

Inventor： Garry M. Steil ,  Sami S. Kanderian, Jr. ,  Martin T. Cantwell ,  Udo Hoss

IPC: A61B5/145 ,  A61B5/00 ,  A61B5/1486 ,  A61B5/1495 ,  A61K38/28 ,  A61M5/142 ,  A61M5/158 ,  A61M5/172 ,  G16H20/10 ,  G16H20/17 ,  G16Z99/00

CPC classification number: A61B5/14532 ,  A61B5/14865 ,  A61B5/4839 ,  A61B5/6849 ,  A61B5/7275 ,  A61K38/28 ,  A61M5/14244 ,  A61M5/1723 ,  C12Y301/01003 ,  G16H20/17 ,  G16Z99/00 ,  A61B5/1495 ,  A61M2005/14208 ,  A61M5/158 ,  A61M2005/1581 ,  A61M5/1582 ,  A61M2205/3523 ,  A61M2205/3561 ,  A61M2205/3569 ,  G16H20/10

Abstract: Disclosed herein are techniques related to model predictive control. The techniques may involve generating a desired glucose trajectory that approaches a desired steady state setpoint from a current glucose value over a prediction horizon. The techniques may involve generating a plurality of insulin delivery patterns. Each insulin delivery pattern may correspond to an amount of insulin to be delivered over a control horizon. The techniques may involve generating a plurality of predicted glucose trajectories over the control horizon. Each predicted glucose may be generated based on the current glucose value and a respective insulin delivery pattern. The techniques may involve comparing the desired glucose trajectory against each predicted glucose trajectory and selecting a predicted glucose trajectory that is more similar to the desired glucose trajectory than any other predicted glucose trajectory. The techniques may involve determining an insulin delivery pattern used to generate the selected predicted glucose trajectory.