Medical device for predicting a user's future glycemic state

• US 7,695,434 B2
• Filed: 10/19/2007
• Issued: 04/13/2010
• Est. Priority Date: 10/19/2007
• Status: Active Grant

• Current Assignee: Cilag Gmbh International (Johnson & Johnson)
• Original Assignee: LifeScan Scotland Limited (Johnson & Johnson)
• Inventors: Malecha, Michael
• Primary Examiner(s): Marmor, II; Charles A
• Assistant Examiner(s): JANG, CHRISTIAN YONGKYUN
• Application Number:
  US11/875,563
• Publication Number:
  US 20090105573A1
• Time in Patent Office: 907 Days
• Field of Search: 600/316, 600/319, 600/347, 600/365
• US Class Current: 600/365
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/7264 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: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A medical device for predicting a user'"'"'s future glycemic state includes a memory module, a processor module and a user alert module. The memory module is configured to receive and store a plurality of glucose concentrations as a function of time that were generated by a user'"'"'s use of a continuous glucose monitor. The processor module is configured to derive first and second glucose prediction equations that are fits to the plurality of glucose concentrations stored in the memory module with the fits being based on first and second mathematical models, respectively. The processor module is also configured to calculate first and second predicted glucose concentrations at a future time using the first and second glucose prediction equations, respectively, and to also calculate an average predicted glucose concentration and a merit index based on the first and second predicted glucose calculations. The processor module is further configured to input the plurality of glucose concentrations as a function of time, the average predicted glucose concentration and the merit index into a trained model (e.g., a Hidden Markov Model) that outputs a set of glucose concentration probabilities for the future time and to then predict the user'"'"'s future glycemic state based on the set of glucose concentration probabilities. The user alert module is configured to alert the user in a manner dependent on the predicted user'"'"'s future glycemic state.

Claims

1. A medical device for predicting a user'"'"'s future glycemic state comprising:

a memory module;

a processor module; and

a user alert module,

wherein the memory module is configured to receive and store a plurality of glucose concentrations as a function of time that were generated by a user'"'"'s use of a continuous glucose monitor, and

wherein the processor module is configured to;

derive a first glucose prediction equation that is a fit to the plurality of glucose concentrations as a function of time stored in the memory module, the fit being based on a first mathematical model;

derive a second glucose prediction equation that is a fit to the plurality of glucose concentrations as a function of time, the fit being based on a second mathematical model;

calculate a first predicted glucose concentration g1(t_f) at a predetermined future time using the first glucose prediction equation;

calculate a second predicted glucose concentration g2(t_f) at the predetermined future time using the second glucose prediction equation;

calculate an average predicted glucose concentration and a merit index M based on the first and second predicted glucose calculations;

input the plurality of glucose concentrations as a function of time, the average predicted glucose concentration and the merit index into a trained model that outputs a set of glucose concentration probabilities for the predetermined future time,

predict the user'"'"'s future glycemic state based on the set of glucose concentration probabilities;

wherein the user alert module is configured to alert the user in a manner dependent on the predicted user'"'"'s future glycemic state; and

wherein the processor module calculates the merit index using the following algorithms;

M=g2(t_f)−

g1(t_f)/g2(t_f), if g2(t_f)≧

g1(t_f)

M=g1(t_f)−

g2(t_f)/g1(t_f), if g2(t_f)<

g1(t_f).

2. The medical device of claim 1 wherein the set of glucose concentration probabilities is centered on the average predicted glucose concentration and has a range that is dependent on the merit index.

5. The medical device of claim 1 wherein the memory module stores a plurality of glucose concentrations as a function of time is in the range of seven to thirty glucose concentrations as a function of time.

6. The medical device of claim 1, in which the processor employs the predetermined future time point is a time in the range of about 10 minutes to about 30 minutes after the predetermined time duration.

7. The medical device of claim 1, in which the interval is in the range from about 0.2 minutes to about 1 minute.

3. The medical device of 1 wherein the processor predicts the probability that the user will be in a hypoglycemic state at the predetermined future time based on the set of glucose concentration probabilities.

4. The medical device of claim 3, in which the user alert module alerts the user when the probability of the user being in a hypoglycemic state is greater than a predetermined threshold probability.