HUMAN METABOLIC CONDITION MANAGEMENT

• US 20170242975A1
• Filed: 10/15/2015
• Published: 08/24/2017
• Est. Priority Date: 10/17/2014
• Status: Active Grant

• Current Assignee: Bradley E. Kahlbaugh
• Original Assignee: Bradley E. Kahlbaugh
• Inventors: Kahlbaugh, Bradley E.
• Granted Patent: US 10,529,454 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• 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: A61M 2005/1726 the body parameters being measured at, or proximate to, the infusion site
• CPC Class Codes: A61M 5/14244 adapted to be carried by the patient, e.g. portable on the body
• 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 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 40/63 for local operation
• 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

Systems and methods for extracting blood glucose patterns and suggesting a behavior may include receiving, at a computing device comprising a processor, temporal data including information regarding glucose readings; identifying, by the computing device, at least one pattern based on metabolite levels extracted from the temporal data the model including variables corresponding to each of the patterns; formulating, by the computing device, a model for predicting a metabolic response; and storing the model on a data storage device. Based on the model, the behavior may be suggested to maintain a blood glucose level within a desired range.

Claims

1. A method comprising:

receiving, at a computing device comprising a processor, temporal data including information regarding metabolite readings;

identifying, by the computing device, at least one pattern based on metabolite levels extracted from the temporal data;

formulating, by the computing device, a model for predicting a metabolite level change, the model including variables corresponding to the at least one identified pattern; and

storing the model on a data storage device.

2. The method of claim 1, wherein the information regarding metabolite readings includes glucose readings that include information regarding at least one of a medication taken, a food item ingested, or an activity undertaken.

3. The method of claim 1, wherein the at least one identified pattern includes an increase in a blood glucose level.

4. The method of claim 3, wherein the increase in the blood glucose level is due to ingestion of a food item or administering a medication.

5. The method of claim 1, wherein the at least one identified pattern include a decrease in a blood glucose level.

6. The method of claim 5, wherein the decrease in the blood glucose level is due to administering a medication or undertaking an activity.

7. The method of claim 1, wherein the temporal data includes exercise activities engaged in by a user that result in a change in a blood glucose level.

8. The method of claim 1, wherein the temporal data includes the metabolite levels recorded over a time interval.

9. The method of claim 1, wherein the model is formulated according to at least one of a statistical analysis, a Monte Carlo simulation, a single variable regression analysis, and a multivariable regression analysis.

10. The method of claim 1, further comprising validating the model against a subsample of the temporal data not used to formulate the model.

11. The method of claim 1, further comprising:

receiving updated temporal data;

identifying an updated pattern based on metabolite levels extracted from the updated temporal data; and

validating the model against using the updated pattern.

12. The method of claim 11, further comprising formulating a new model when the model does not validate against the updated pattern.

13. The method of claim 1, further comprising:

receiving a behavior input from a user, the behavior input including an indication of an ingestion of a food item or the administration of a medication;

determining a metabolite level change based at least in part on the model;

selecting a recommended behavior; and

outputting the recommended behavior to a display associated with the computing device.

14. The method of claim 13, wherein the recommended behavior includes insulin stacking.

16. The system of claim 14, wherein the model comprises a sigmoid form.

17. The system of claim 14, wherein the operations further comprise validating the model against a subsample of the temporal data not used to formulate the model.

18. The system of claim 14, wherein the operations further comprise:

receiving updated temporal data;

identifying an updated pattern based on metabolite levels extracted from the updated temporal data; and

validating the model against using the updated pattern.

19. The system of claim 17, wherein the operations further comprise formulating a new model when the model does not validate against the updated pattern.

20. The system of claim 14, further comprising a display and wherein the operations further comprise:

receiving a behavior input from a user, the behavior input including an indication of an ingestion of a food item or the administration of a medication;

determining a metabolite level change based at least in part on the model;

selecting a recommended behavior; and

outputting the recommended behavior to the display.

21. The system of claim 14, wherein the system is one of a blood glucose meter, an insulin pump, and a smartphone.

15. A system comprising:

a processor; and

a memory that store instructions that, when executed by the processor, cause the processor to perform operations comprising;

receiving temporal data including information regarding metabolite readings,

identifying at least one pattern based on metabolite levels extracted from the temporal data,

formulating a model for predicting a metabolite level change, the model including variables corresponding to the at least one identified pattern, and

storing, to the memory, the model.