METHOD AND SYSTEM FOR AUTOMATIC MONITORING OF DIABETES RELATED TREATMENTS

• US 20120123234A1
• Filed: 02/25/2010
• Published: 05/17/2012
• Est. Priority Date: 02/26/2009
• Status: Abandoned Application

• Current Assignee: Dreamed Diabetes Ltd.
• Original Assignee: Dreamed Diabetes Ltd.
• Inventors: Atlas, Eran, Nimri, Revital, Miller, Shahar, Grunberg, Eli Aviram, Phillip, Moshe
• Application Number:
  US13/203,273
• Publication Number:
  US 20120123234A1
• Time in Patent Office: Days
• US Class Current: 600/365
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61M 2005/14208 with a programmable infusion control system, characterised by the infusion program
• CPC Class Codes: A61M 2005/14296 Pharmacokinetic models
• 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: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

The present invention discloses a monitoring system and method for use in monitoring diabetes treatment of a patient. The system comprises a control unit comprising a first processor module for processing measured data indicative of blood glucose level and generating first processed data indicative thereof, a second processor module comprising at least one fuzzy logic module; the second processor module receives input parameters corresponding to the measured data, the first processed data and a reference data including individualized patient'"'"'s profile related data, to individualized patient'"'"'s treatment history related data and processes the received data to produce at least one qualitative output parameter indicative of patient'"'"'s treatment parameters, such that the second processor module determines whether any of the treatment parameters is to be modified.

Claims

1. A monitoring system for use in monitoring diabetes treatment of a patient, the system comprising:

a control unit comprising

a first processor module for processing measured data indicative of blood glucose level and generating first processed data indicative thereof,

a second processor module comprising at least one fuzzy logic module;

said fuzzy logic module receives input parameters corresponding to the measured data, the first processed data and a reference data including individualized patient'"'"'s profile related data, individualized patient'"'"'s treatment history related data, processes the received parameters to produce at least one qualitative output parameter indicative of patient'"'"'s treatment parameters;

such that said second processor module determines whether any of the treatment parameters is to be modified.

2. The system of claim 1, wherein said second processor module provides control to range output treatment suggestion.

3. The system of claim 2, wherein said control to range output treatment suggestion comprises at least one of insulin basal rate, insulin bolus or glucagon bolus.

4. The system of claim 1, wherein said input parameters includes at least one of the following input parameters:

past blood glucose level trend, current blood glucose level, future blood glucose level trend, future blood glucose level.

5. The system of claim 1, wherein said at least one fuzzy logic module comprises a set of rules and at least one fuzzy engine utilizing one or more member functions modeled for translating the input parameters into at least one qualitative output parameter.

6. The system of claim 1, wherein said at least one output parameter of the fuzzy logic module comprises data indicative of at least one of bolus glucagon, bolus insulin and basal insulin treatment.

7. The system of claim 1, wherein said control unit comprises a third processor module receiving said at least one qualitative output parameter of the fuzzy logic module and processing said at least one output parameter to determine whether any of the treatment parameters is to be modified.

8. The system of claim 7, wherein said control unit determines amount of dosing of insulin and/or glucagon to be delivered.

9. The system of claim 8, wherein the third processor receives the control to range output treatment suggestion, and determine said amount in accordance with a glucose target of the patient'"'"'s profile.

10. The system of claim 9, wherein said amount is adjusted in accordance with at least one of patient'"'"'s insulin or glucagon pharmacodynamics and said measured data.

11. The system of claim 1, wherein said control unit is associated with a drug injection device and is configured and operable to control the operation of said drug injection device.

12. The system of claim 1, comprising a data transceiver for receiving at least one of said reference data and said measured data.

13. The system of claim 12, wherein said data transceiver is operable to transmit said at least one output parameter of the control unit to said drug injection device.

14. The system of claim 1, wherein said individualized patient'"'"'s profile related data comprises parameters selected from at least one of insulin sensitivity, glucagon sensitivity, basal plan, insulin/glucagon pharmacokinetics associated data, glucose target level or target range level, and insulin/glucagon activity model.

15. The system of claim 1, wherein said system is operable to update and/or calibrate said individualized patient'"'"'s profile related data during treatment or during monitoring procedure.

16. The system of claim 1, wherein said individualized patient'"'"'s treatment history related data comprises patient'"'"'s insulin delivery regimen given to the patient at different hours of the day.

17. The system of claim 1, wherein each rule is associated with a contribution factor.

18. The system of claim 1, wherein said second processor module comprises a fuzzy logic module operable in response to an event being invoked by a detector module analyzing at least one pattern of glucose levels indicative of at least one event.

19. The system of claim 18, wherein said event comprises at least one of sleep, meal, exercise and disease event or rest.

20. The system of claim 18, wherein said system is configured and operable to alternate between at least two fuzzy logic modules, each handling a different event.

21. The system of claim 20, wherein said second processor module operable as a meal treatment module is configured to monitor the blood glucose level.

22. The system of claim 21, wherein said input parameters further includes at least one of the following input parameters:

time elapsed between detected special events, blood glucose level with respect to said special event.

23. The system of claim 1, wherein said measured data is obtained at a certain time.

24. The system of claim 23, wherein said measured data includes current and past glucose levels relative to said certain time.

25. A method for automatic monitoring of diabetes-related treatment, the method comprises:

obtaining a reference data including individualized patient'"'"'s profile related data, individualized patient'"'"'s treatment history related data;

analyzing measured data generated by at least one of drug delivery devices and glucose measurement devices; and

deciding about treatment modification in accordance with said reference data by controlling the operation of the drug injection devices to enable real-time automatic individualized monitoring of the treatment procedure.

26. The method of claim 25, wherein said deciding about treatment modification comprises determining said treatment modification in accordance with said individualized patient'"'"'s treatment history related data.

27. The method of claim 26, wherein said obtaining of said individualized patient'"'"'s profile related data comprises obtaining parameters selected from at least one of insulin sensitivity, glucagon sensitivity, basal plan, insulin/glucagon pharmacokinetics associated data, glucose target level or target range level, and insulin activity model.

28. The method of claim 27, comprising updating said patient'"'"'s profile related data in accordance with the treatment.

29. The method of claim 25, wherein said obtaining of individualized patient'"'"'s treatment history related data comprises obtaining at least one of patient'"'"'s insulin delivery regimen given to the patient at different hours of the day.

30. The method of claim 25, wherein said analyzing data comprises processing measured data indicative of blood glucose level and generating first processed data indicative thereof, and applying at least one fuzzy logic model to input parameters corresponding to the measured data, the first processed data and said reference data, to produce at least one qualitative output parameter indicative of patient'"'"'s treatment.

31. The method of claim 30, wherein said applying at least one fuzzy logic model to input parameters corresponding to the measured data comprises classifying glucose blood trends in different categories.

32. The method of claim 30, comprising applying a prediction model for predicting glucose trend in blood based on the measured glucose level.

33. The method of claim 30, wherein said applying at least one fuzzy logic model comprises alternating between at least two fuzzy logic models, each being configured to handle a different event.

34. The method of claim 25, wherein deciding about treatment modification comprises at least one of the followings:

controlling an individualized basal plan;

controlling a insulin/glucagon sensitivity indicative of the correction of the current blood glucose level to a target level, correction of carbohydrates and of the amount of insulin and/or glucagon to be delivered;

controlling the individualized blood glucose target level;

controlling the insulin and/or glucagon pharmacokinetics settings.

35. The method of claim 34, wherein said controlling an individualized basal plan comprises obtaining a series of individualized basal treatment rates as a function of time;

obtaining said measured data;

determining an individualized time delay between a basal treatment rate of the series of individualized basal treatment rates and changes in the glucose level to thereby obtain a series of basal treatment rates and corresponding changes in glucose level at a time delay;

selecting a basal plan which incorporates the basal rates minimizing a change in the glucose level.

36. The method of claim 25, wherein said analyzing data comprises determining the probability of the patient to be in a special event as a function of time.

37. The method of claim 36, wherein determining said special event comprises determining at least one of sleep, meal, exercise, disease or rest event.

38. A method for use in automatic monitoring of diabetes-related treatment, the method comprises:

analyzing open-loop measured data generated by at least one of drug delivery devices and glucose measurement devices and determining patient'"'"'s initial treatment profile;

receiving continuously measured data generated by at least one of drug delivery devices and glucose measurement devices;

applying self-learning procedure for updating said patient'"'"'s initial treatment profile during closed loop treatment thereby monitoring of the diabetes-related treatment.

39. The method of claim 38, wherein said patient'"'"'s initial treatment profile comprises at least one of insulin sensitivity indicative of the correction of the current blood glucose level to a target level, correction of carbohydrates and of the amount of insulin and/or glucagon to be delivered, basal plan, insulin/glucagon pharmacokinetics associated data, glucose target level or target range level.

40. The method of claim 38, wherein determining the insulin sensitivity comprises using at least one of the following parameters:

carbohydrate consumed by the patient, measured data, and patient'"'"'s treatment.

41. The method of claim 38, wherein said determining patient'"'"'s initial treatment profile comprises determining the amount of insulin active in the blood.

42. The method of claim 41, wherein said determining the amount of insulin active in the blood comprises determining said amount as a function of a special event.

43. A method for determining insulin basal plan from a series of basal treatment rates for a patient in need thereof, comprises:

obtaining a series of basal treatment rates as a function of time;

obtaining measured data of glucose level in the patient as a function of time;

determining a series of changes in glucose levels as a function of time;

determining the personal time delay of the patient estimated from the series of basal treatment rates and the series of changes in glucose levels, thereby obtaining a series of basal treatment rates and corresponding changes of glucose level in the patient; and

selecting a basal plan which incorporates the basal rates that minimizes a change in the glucose level.

44. A method for determining a insulin sensitivity for use in close-loop treatment of a patient'"'"'s need thereof, comprising:

obtaining a first glucose sensor reading and a second glucose sensor reading defining a time window;

obtaining the difference between the first and second glucose sensor readings;

adjusting the difference between the first and second glucose sensor readings by estimating glucose derived from the consumed carbohydrate within the time window;

thereby obtaining an adjusted glucose amount; and

determining the insulin sensitivity in accordance to the relation between the adjusted glucose amount and insulin bolus provided during the time window.

45. The method of claim 44, wherein said time window includes an open loop session.

46. A method of claim 44, wherein said adjusting comprises determining a coefficient defining the proportion of consumed carbohydrate to glucose derived thereby.

47. A method of claim 44, wherein said determining of the insulin sensitivity comprising modifying said insulin sensitivity in accordance with proportion between minimum sensor reading during the time window and the lowest blood glucose reading recorded in neither during hypoglycaemia nor hypoglycaemia.

48. A method of claim 47, wherein said modifying of the insulin sensitivity comprises modifying the insulin sensitivity according to the maximum sensor reading in a time interval prior to the obtaining of the minimum sensor reading.

49. A method of claim 48, wherein said modifying of the insulin sensitivity comprises modifying the insulin sensitivity according to a histogram representing the occurrence of measured glucose level of the patient during a certain time window.