System, method and article for controlling the dispensing of insulin

• US 8,548,544 B2
• Filed: 06/19/2007
• Issued: 10/01/2013
• Est. Priority Date: 06/19/2006
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: Dose Safety
• Inventors: Kircher, Robert C. Jr., Mauseth, Richard S., Bishop, Jason N., Matheson, Donald P., Bhatia, Suray, Bilmes, Jeff A.
• Primary Examiner(s): LaBalle, Clayton E
• Assistant Examiner(s): Chang, Fang-Chi
• Application Number:
  US12/305,582
• Publication Number:
  US 20100292634A1
• Time in Patent Office: 2,296 Days
• Field of Search: 600/316, 600/347, 600/365
• US Class Current: 600/316
• 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. A blood glucose control system, comprising:

a data input subsystem configured to receive and store blood glucose-related data;

an output subsystem configured to generate a control signal to control dispensing of insulin based on the received blood glucose-related data and fuzzy-logic control parameters; and

a parameter adjuster configured to adjust the fuzzy-logic control parameters based on the received and the stored blood-glucose related data.

2. The blood glucose control system of claim 1, further comprising:

a blood glucose level predictor configured to predict blood glucose levels based on the received blood glucose-related data.

3. The blood glucose control system of claim 2 wherein the blood glucose level predictor is configured to predict blood glucose levels based on the stored blood glucose-related data.

4. The blood glucose control system of claim 2 wherein the blood glucose level predictor is configured to predict a subsequent blood glucose level based on a previously predicted blood glucose level.

5. The blood glucose control system of claim 2 wherein the blood glucose level predictor is configured to predict blood glucose levels based on a maintained fuzzy-logic rules matrix.

6. The blood glucose control system of claim 1, further comprising:

an insulin dispenser coupled to the output subsystem.

7. The blood glucose control system of claim 1 wherein the data input subsystem comprises a blood glucose sensor.

8. The blood glucose control system of claim 1 wherein the control system is configured to selectively transition between a post-meal correction mode and a fasting mode.

9. The blood glucose control system of claim 8 wherein the control system is configured to selectively transition from the post-meal correction mode to the fasting mode when a fasting criteria is satisfied.

10. The blood glucose control system of claim 8 wherein the control system is configured to selectively transition from the fasting mode to the post-meal correction mode when a prandial event is detected.

11. The blood glucose control system of claim 8 wherein the control system is configured to selectively transition from the fasting mode to a user-input mode when a prandial event is detected.

12. The blood glucose control system of claim 1 wherein the fuzzy-logic control parameters comprise fuzzy-logic multipliers.

13. The blood glucose control system of claim 12 wherein the parameter adjuster is configured to selectively adjust a fuzzy logic rules matrix.

14. The blood glucose control system of claim 13 wherein the parameter adjuster is configured to selectively adjust the fuzzy logic rules matrix according to a dosing matrix coherency policy.

15. The system of claim 1 wherein the parameter adjuster comprises at least one of a neural network and a dynamic Bayesian network.

16. A system for controlling the dispensing of insulin, comprising:

a data input subsystem configured to receive blood glucose-related data;

a blood glucose level predictor coupled to the input system and configured to predict blood glucose levels based on the received data; and

an output subsystem configured to use fuzzy logic to generate a control signal to control the dispensing of insulin based on the received data and the predicted blood glucose levels.

17. The system of claim 16, further comprising:

a model parameter adjuster, wherein the blood glucose level predictor is configured to predict blood glucose levels based on parameters of a statistical model and the model parameter adjuster is configured to adjust the parameters of the statistical model.

18. The system of claim 16, further comprising:

a control parameter adjuster, wherein the output system is configured to generate the control signals based on control parameters and the control parameter adjuster is configured to adjust the control parameters.

19. The system of claim 18 wherein the control parameter adjuster is configured to adjust the control parameters based on a comparison of the predicted blood glucose levels to the received data.

20. The system of claim 16, further comprising:

a memory configured to store blood glucose-related data, wherein the blood glucose level predictor is configured to predict blood glucose levels based on data stored in the memory.

21. The system of claim 16 comprising at least one of a neural network and a dynamic Bayesian network.

22. The system of claim 16 wherein the blood glucose level predictor comprises at least one of a neural network and a dynamic Bayesian network.

23. A blood glucose control system, comprising:

a data input subsystem configured to receive sensed indications of blood glucose levels;

a parameter adjuster configured to adjust fuzzy logic parameters based on the sensed indications of blood glucose levels; and

an output subsystem configured to generate a control signal to control dispensing of insulin based on the sensed indications of blood glucose levels and the fuzzy logic parameters.

24. The blood glucose control system of claim 23 wherein the parameter adjuster is configured to selectively adjust a fuzzy logic rules matrix.

25. The blood glucose control system of claim 24 wherein the parameter adjuster is configured to selectively adjust the fuzzy logic rules matrix according to a dosing matrix coherency policy.

26. The blood glucose control system of claim 24 wherein the output subsystem is configured to predict blood glucose levels based on the maintained fuzzy-logic rules matrix and to generate the control signal based on the predicted blood glucose levels.

27. The blood glucose control system of claim 23, further comprising:

a sensor for sensing indications of blood glucose levels coupled to the data input system.

28. A blood glucose control system, comprising:

means for sensing indications of blood glucose levels;

means for predicting blood glucose levels based on sensed indications of blood glucose levels communicatively coupled to the means for sensing indications of blood glucose levels; and

means for applying fuzzy logic to generate control signals for controlling dispensing of insulin, wherein the means for predicting is configured to maintain a fuzzy-logic rules matrix of the means for applying fuzzy logic based on the sense indications.

29. The blood glucose control system of claim 28, further comprising:

means for dispensing insulin.

30. The blood glucose control system of claim 28 wherein the means for predicting is configured to maintain the fuzzy-logic rules matrix based on the sensed indications and previously predicted blood glucose levels.

31. The blood glucose control system of claim 28 wherein the means for predicting is configured to predict blood glucose levels based on the maintained fuzzy-logic rules matrix.

32. The system of claim 28, wherein the means for predicting includes a neural network.