System, method and article for controlling the dispensing of insulin

• US 9,694,133 B2
• Filed: 05/13/2015
• Issued: 07/04/2017
• Est. Priority Date: 06/19/2006
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: Dose Safety
• Inventors: Mauseth, Richard S., Bishop, Jason N., Matheson, Donald P., Bhatia, Suray, Kircher, Jr., Robert C., Bilmes, Jeff A.
• Primary Examiner(s): Fuller, Rodney
• Application Number:
  US14/710,851
• Publication Number:
  US 20160022904A1
• Time in Patent Office: 783 Days
• Field of Search: 600316
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61M 2230/005 Parameter used as control input for the apparatus
• CPC Class Codes: A61M 2230/201 Glucose concentration
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/13 delivered from dispensers
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A system and method for automatically adjusting parameters for predicting blood glucose levels and/or controlling the dispensing of insulin. In one embodiment, the system is a stand-alone system. In one embodiment, the system is part of a system for controlling the dispensing of insulin.

Claims

1. An integrated circuit, comprising:

one or more memories; and

circuitry coupled to the one or more memories, wherein the circuitry, in operation;

adjusts fuzzy-logic control parameters based on received and retrieved blood glucose-related data; and

generates a control signal to control dispensing of insulin based on the received blood glucose-related data and the fuzzy-logic control parameters, wherein the circuitry, in operation, predicts blood glucose levels based on the received blood glucose-related data.

2. The integrated circuit of claim 1 wherein the circuitry, in operation, predicts blood glucose levels based on the retrieved blood glucose-related data.

3. The integrated circuit of claim 1 wherein the circuitry, in operation, selectively transitions between a post-meal correction protocol and a fasting protocol.

4. The integrated circuit of claim 1 wherein the circuitry, in operation, transitions from a post-meal correction protocol to a fasting protocol when a fasting criteria is satisfied.

5. The integrated circuit of claim 1 wherein the circuitry, in operation, transitions from a fasting protocol to a post-meal correction protocol when a prandial event is detected.

6. The integrated circuit of claim 1 wherein the circuitry, in operation, transitions from a fasting protocol to a user-input protocol when a prandial event is detected.

7. The integrated circuit of claim 1 wherein the fuzzy-logic control parameters comprise fuzzy-logic multipliers.

8. The integrated circuit of claim 1 wherein the predicting blood glucose levels comprises predicting a subsequent blood glucose level based on a previously predicted blood glucose level.

9. The integrated circuit of claim 1 wherein the circuitry comprises at least one of a neural network and a dynamic Bayesian network.

10. A device, comprising:

one or more memories; and

one or more processors, which in operation;

predict blood glucose levels based on received blood glucose-related data; and

generate, using fuzzy logic, a control signal to control dispensing of insulin based on the received data and the predicted blood glucose levels, wherein the one or more processors, in operation;

predict blood glucose levels based on parameters of a statistical model; and

adjust the parameters of the statistical model.

11. The device of claim 10, comprising an insulin dispenser, which, in operation, is controlled based on the control signal.

12. The device of claim 10 wherein the adjusting the parameters of the statistical model occurs in real-time as blood glucose-related data is received.

13. The device of claim 10 wherein the one or more processors, in operation:

generate the control signal based on control parameters; and

adjust the control parameters.

14. The device of claim 10 wherein,

the one or more memories store historical blood glucose-related data;

and

the one or more processors, in operation, predict blood glucose levels based on stored historical blood glucose-related data.

15. A non-transitory computer-readable memory medium having contents which configure one or more processing devices to perform a method, the method comprising:

predicting blood glucose levels based on received blood glucose-related data; and

generating, using fuzzy logic, a control signal to control dispensing of insulin based on the received data and the predicted blood glucose levels, wherein the method comprises;

predicting blood glucose levels based on control parameters; and

adjusting the control parameters.

16. The medium of claim 15 wherein the method comprises:

storing historical blood glucose-related data; and

predicting blood glucose levels based on stored historical blood glucose-related data.