System, method and article for controlling the dispensing of insulin
An integrated circuit includes circuitry to control a process. The process includes adjusting fuzzy-logic control parameters based on received and retrieved blood glucose-related data, predicting blood glucose levels based on the received blood-glucose-related data, and generating control signals to control dispensing of insulin based on the received blood glucose-related data and the fuzzy-logic control parameters. The process may include predicting blood glucose levels based on the retrieved blood glucose-related data. The process may include transitioning between a post-meal correction protocol and a fasting protocol. The process may include transitioning from a post-meal correction protocol to a fasting protocol when a fasting criteria is satisfied.
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;
a parameter adjuster configured to adjust the fuzzy-logic control parameters based on the received and the stored blood-glucose related data; and
a pen-dispensing device coupled to the output subsystem.
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 , wherein the data input subsystem comprises a blood glucose sensor.
7. 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.
8. The blood glucose control system of claim 7 , 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.
9. The blood glucose control system of claim 7 , 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.
10. The blood glucose control system of claim 9 , wherein the pen-dispensing device is configured to dispense a bolus insulin dose in the post-meal correction mode.
11. The blood glucose control system of claim 9 , wherein the pen-dispensing device is configured to adjust a basal insulin dose in the post-meal correction mode.
12. The blood glucose control system of claim 7 , wherein the control system is configured to selectively transition from the fasting mode to a user-input mode when a prandial event is detected.
13. The blood glucose control system of claim 1 wherein the fuzzy-logic control parameters comprise fuzzy-logic multipliers.
14. The blood glucose control system of claim 13 , wherein the parameter adjuster is configured to selectively adjust a fuzzy logic rules matrix.
15. The blood glucose control system of claim 14 , wherein the parameter adjuster is configured to selectively adjust the fuzzy logic rules matrix according to a dosing matrix coherency policy.
16. The blood glucose system of claim 1 wherein the parameter adjuster comprises at least one of a neural network and a dynamic Bayesian network.
17. The blood glucose control system of claim 1 , wherein the control signal causes the pen-dispensing device to dispense or adjust a bolus and/or a basal dose.
18. A computer-implemented method, comprising:
receiving blood glucose-related data; retrieving stored blood glucose-related data; adjusting fuzzy-logic control parameters based on the received and the retrieved blood glucose-related data;
generating a control signal to control dispensing of insulin based on the received blood glucose-related data and fuzzy-logic control parameters, wherein the receiving, the retrieving, the adjusting and the generating are performed by one or more configured computing systems; and
dispensing insulin through a pen-dispensing device based on the control signal.
19. The computer-implemented method of claim 18 , wherein the control signal causes the pen-dispensing device to dispense or adjust a bolus and/or a basal dose.
20. The computer-implemented method of claim 18 , comprising predicting blood glucose levels based on the received blood glucose-related data.
21. The computer-implemented method of claim 20 , comprising predicting blood glucose levels based on the retrieved blood glucose-related data.
22. The method of claim 20 , wherein the predicting blood glucose levels comprises predicting a subsequent blood glucose level based on a previously predicted blood glucose level.
23. The method of claim 20 , wherein the predicting blood glucose levels comprises maintaining a fuzzy-logic rules matrix.
24. The computer-implemented method of claim 18 , comprising sensing blood glucose levels.
25. The computer-implemented method of claim 18 , comprising selectively transitioning between using a post-meal correction protocol and a fasting protocol.
26. The computer-implemented method of claim 18 , comprising transitioning from a post-meal correction protocol to a fasting protocol when a fasting criteria is satisfied.
27. The computer-implemented method of claim 18 , comprising transitioning from a fasting protocol to a post-meal correction protocol when a prandial event is detected.
28. The computer-implemented method of claim 18 , wherein the control signal causes the pen-dispensing device to dispense a bolus insulin dose in the post-meal correction mode.
29. The computer-implemented method of claim 18 , wherein the control signal causes the pen-dispensing device is configured to adjust a basal insulin dose in the post-meal correction mode.
30. The computer-implemented method of claim 18 , comprising transitioning from a fasting protocol to a user-input protocol when a prandial event is detected.
31. The computer-implemented method of claim 18 , wherein the fuzzy-logic control parameters comprise fuzzy-logic multipliers.
32. The computer-implemented method of claim 31 , wherein adjusting the fuzzy-logic control parameters comprises selectively adjusting a fuzzy logic rules matrix.
33. The method of claim 32 , wherein the selectively adjusting the fuzzy logic rules matrix comprises applying a dosing matrix coherency policy.
34. The method of claim 32 , wherein the adjusting fuzzy-logic control parameters comprises using at least one of a neural network and a dynamic Bayesian network.
35. The method of claim 32 , wherein the adjusting fuzzy-logic control parameters occurs in real-time.
36. A device, comprising:
one or more memories;
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;
adjust the parameters of the statistical model; and
an insulin pen dispenser, which, in operation, is controlled based on the control signal.
37. The device of claim 36 , wherein the adjusting the parameters of the statistical model occurs in real-time as blood glucose-related data is received.
38. The device of claim 36 , wherein the one or more processors, in operation: generate the control signal based on control parameters; and adjust the control parameters.
39. The device of claim 36 , 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.
40. The device of claim 36 , wherein the control signal causes the pen-dispensing device to dispense or adjust a bolus and/or a basal dose.