Glucose predictor based on regularization networks with adaptively chosen kernels and regularization parameters

• US 10,307,109 B2
• Filed: 04/20/2012
• Issued: 06/04/2019
• Est. Priority Date: 04/20/2011
• Status: Active Grant

• Current Assignee: Novo Nordisk A/S
• Original Assignee: Novo Nordisk A/S
• Inventors: Randloev, Jette, McKennoch, Samuel, Pereverzyev, Sergei, Sampath, Sivananthan
• Primary Examiner(s): Riggs, II, Larry D
• Application Number:
  US14/112,311
• Publication Number:
  US 20140073892A1
• Time in Patent Office: 2,601 Days
• Field of Search: None
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• 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 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

The invention relates to a method and a device for predicting a glycaemic profile of a subject. A multistage algorithm is employed comprising a prediction setting stage specifying a functional space for the prediction and a prediction execution stage specifying a predicted future glycaemic state of the subject in the functional space as a continuous function of time.

Claims

1. A glucose prediction device comprising:

an input device structured to receive information indicative of a physiologic condition of a subject;

a processing device comprising an adaptive regularization network that is structured to predict a future glucose profile;

an output device structured to convey the future glucose profile; and

an alarm;

wherein the future glucose profile is the glycaemic state of the subject as a continuous function of time;

wherein the adaptive regularization network is adapted to perform a multistage prediction process and comprises a supervising learning machine and a supervised learning machine that allows the future glucose profile to be predicted with irregularly sampled data in the information received by the input device;

wherein (a) the supervising learning machine is adapted to (i) compress the information received by the input device and (ii) run the compressed information through a pre-constructed machine to produce kernel parameters and a regularization parameter; and

(b) the supervised learning machine is adapted to calculate the future glucose profile as a function of the information received by the input device and the kernel parameters and the regularization parameter produced by the supervising learning machine; and

wherein the alarm is structured to alert the subject if the future glucose profile includes an impending hypo- or hyperglycaemic event.

2. The device according to claim 1, wherein the pre-constructed machine is produced by a linear fitting, and wherein the kernel parameters and the regularization parameter is based on the linear fitting.

3. The device according to claim 1, wherein the multistage prediction process comprises a reproducing kernel Hilbert space.

4. The device according to claim 3, wherein the reproducing kernel Hilbert space is selected based on a data pool of training data segments, wherein each training data segment is indicative of the physiologic condition of the subject at a point in time.

5. The device according to claim 4, wherein the data pool is obtained from a predetermined data pool.

6. The device according to claim 4, wherein the data pool is obtained from a continuously updated data pool.

7. The device according to claim 1, wherein the information indicative of the physiologic condition of the subject is at least one measurement of a body characteristic.

8. The device according to claim 7, wherein the body characteristic is blood or tissue glucose.

9. The device according to claim 7, wherein the input device is further adapted to receive information related to a therapeutic treatment.

10. The device according to claim 9, wherein the information related to the therapeutic treatment is past delivery of a glucose regulating agent.

11. The device according to claim 1, wherein the input device is further adapted to receive information related to a meal consumed or to be consumed by the subject.

12. A computer-implemented method for predicting a future glucose profile of a subject that is the glycaemic state of the subject as a continuous function of time, the method comprising:

providing a glucose prediction device comprising a processing device;

receiving, by the processing device, information indicative of a physiologic condition of the subject;

specifying, by the processing device, a functional space for the predicted glucose profile by compressing the received information and running the compressed information through a pre-constructed machine to produce kernel parameters and a regularization parameter;

calculating, by the processing device, predicted glucose values as a continuous function of time based on the received information, the kernel parameters, and the initial regularization parameter to thereby produce the predicted future glucose profile of the subject; and

alerting the user, by the glucose prediction device, if the future glucose profile of the subject includes an impending hypo- or hyperglycaemic event.

13. The method according to claim 12, further comprising producing the kernel parameters by a regularized learning algorithm in a reproducing kernel Hilbert space defined by at least approximately minimizing an error function.

14. The computer-implemented method according to claim 12, further comprising producing the pre-constructed machine by

receiving a data pool of training data segments, wherein each training data segment is indicative of a physiologic condition of the subject at respective points in time;

compressing each training data segment to obtain a corresponding set of compressed training data segments;

determining a set of training parameters parameterising the functional space, wherein each training parameter of the training set minimizes a prediction error function indicative of a deviation between a physiologic condition and a predicted physiologic condition, wherein the predicted physiologic condition is predicted based on the functional space parameterised by said training parameter; and

constructing, from the set of training parameters, a non-linear mapping between compressed input data segments and a set of kernel parameters parameterising the respective kernels.

15. The method according to claim 14, wherein constructing the non-linear mapping comprises determining a mechanism for determining a Tikhonov regularization parameter.

16. The method according to claim 14, wherein the prediction error function penalises a delay or failure in the prediction of physiological events classified as dangerous more heavily than a delay or failure in the prediction of physiological events classified as normal.

17. The method according to claim 14, wherein the data pool is obtained from a predetermined data pool.

18. The method according to claim 14, wherein the data pool is obtained from a continuously updated data pool.

19. The method according to claim 12, wherein the information indicative of the physiologic condition of the subject is at least one measurement of a body characteristic.

20. The method according to claim 19, wherein the body characteristic is blood or tissue glucose.

21. The method according to claim 12, further comprising receiving, by the processing device, information related to a therapeutic treatment.

22. The method according to claim 21, wherein the information related to the therapeutic treatment is past delivery of a glucose regulating agent.

23. The method according to claim 12, further comprising receiving, by the processing device, information related to a meal consumed or to be consumed by the subject.

24. A glucose prediction device comprising:

an input device structured to receive information indicative of a physiologic condition of a subject, wherein the information comprises blood or tissue glucose measurements;

a processing device comprising an adaptive regularization network that is structured to predict a future glucose profile;

an output device structured to convey the future glucose profile; and

an alarm;

wherein the future glucose profile is the glycaemic state of the subject as a continuous function of time;

wherein the adaptive regularization network is adapted to perform a multistage prediction process and comprises a supervising learning machine and a supervised learning machine that allows the future glucose profile to be predicted with irregularly sampled data in the blood or tissue glucose measurements received by the input device;

wherein (a) the supervising learning machine is adapted to (i) compress the information received by the input device and (ii) run the compressed information through a pre-constructed machine to produce kernel parameters and a regularization parameter; and

(b) the supervised learning machine is adapted to calculate the future glucose profile as a function of the information received by the input device and the kernel parameters and the regularization parameter produced by the supervising learning machine; and

wherein the alarm is structured to alert the subject if the future glucose profile includes an impending hypo- or hyperglycaemic event.

25. The device according to claim 24, wherein the pre-constructed machine is produced by a linear fitting, and wherein the kernel parameters and the regularization parameter is based on the linear fitting.

26. The device according to claim 24, wherein the multistage prediction process comprises a reproducing kernel Hilbert space.

27. The device according to claim 26, wherein the reproducing kernel Hilbert space is selected based on a data pool of training data segments, wherein each training data segment is indicative of the physiologic condition of the subject at a point in time.

28. The device according to claim 27, wherein the data pool is obtained from a predetermined data pool.

29. The device according to claim 27, wherein the data pool is obtained from a continuously updated data pool.

30. The device according to claim 24, wherein the input device is further adapted to receive information related to a therapeutic treatment.

31. The device according to claim 30, wherein the information related to the therapeutic treatment is past delivery of a glucose regulating agent.

32. The device according to claim 24, wherein the input device is further adapted to receive information related to a meal consumed or to be consumed by the subject.

33. A computer-implemented method for predicting a future glucose profile of a subject that is the glycaemic state of the subject as a continuous function of time, the method comprising:

providing a glucose prediction device comprising a processing device;

receiving, by the processing device, information indicative of a physiologic condition of the subject, wherein the information comprises blood or tissue glucose measurements;

specifying, by the processing device, a functional space for the predicted glucose profile by compressing the received information and running the compressed information through a pre-constructed machine to produce kernel parameters and a regularization parameter, wherein the kernel parameters are produced by a regularized learning algorithm in a reproducing kernel Hilbert space defined by at least approximately minimizing an error function;

calculating, by the processing device, predicted glucose values as a continuous function of time based on the received information, the kernel parameters, and the initial regularization parameter to thereby produce the predicted future glucose profile of the subject; and

alerting the user, by the glucose prediction device, if the future glucose profile of the subject includes an impending hypo- or hyperglycaemic event.

34. The method according to claim 33, further comprising producing the pre-constructed machine by

receiving a data pool of training data segments, wherein each training data segment is indicative of a physiologic condition of the subject at respective points in time;

compressing each training data segment to obtain a corresponding set of compressed training data segments;

determining a set of training parameters parameterising the functional space, wherein each training parameter of the training set minimizes a prediction error function indicative of a deviation between a physiologic condition and a predicted physiologic condition, wherein the predicted physiologic condition is predicted based on the functional space parameterised by said training parameter; and

constructing, from the set of training parameters, a non-linear mapping between compressed input data segments and a set of kernel parameters parameterising the respective kernels.

35. The method according to claim 34, wherein constructing the non-linear mapping comprises determining a mechanism for determining a Tikhonov regularization parameter.

36. The method according to claim 34, wherein the prediction error function penalises a delay or failure in the prediction of physiological events classified as dangerous more heavily than a delay or failure in the prediction of physiological events classified as normal.

37. The method according to claim 34, wherein the data pool is obtained from a predetermined data pool.

38. The method according to claim 34, wherein the data pool is obtained from a continuously updated data pool.

39. The method according to claim 33, further comprising receiving, by the processing device, information related to a therapeutic treatment.

40. The method according to claim 39, wherein the information related to the therapeutic treatment is past delivery of a glucose regulating agent.

41. The method according to claim 33, further comprising receiving, by the processing device, information related to a meal consumed or to be consumed by the subject.