Human metabolic condition management

• US 10,529,454 B2
• Filed: 10/15/2015
• Issued: 01/07/2020
• Est. Priority Date: 10/17/2014
• Status: Active Grant

• Current Assignee: Bradley E. Kahlbaugh
• Original Assignee: Bradley E. Kahlbaugh
• Inventors: Kahlbaugh, Bradley E.
• Primary Examiner(s): Tomaszewski, Michael
• Application Number:
  US15/518,834
• Publication Number:
  US 20170242975A1
• Time in Patent Office: 1,545 Days
• Field of Search: None
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/7275 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;

validating the model against a subsample of the temporal data not used to formulate the model;

storing the model on a data storage device;

receiving, at the computing device, 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, by the computing device, a metabolite level change based at least in part on the model;

selecting, by the computing device, a recommended behavior;

outputting the recommended behavior to a display associated with the computing device; and

performing the recommended behavior by the user.

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 the food item or administering the medication.

5. The method of claim 1, wherein the at least one identified pattern includes 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 the medication or undertaking an activity.

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

8. The method of claim 1, wherein the temporal data includes t 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, wherein receiving the temporal data includes receiving updated temporal data, the method further comprising:

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

wherein validating the model includes validating the model against the updated pattern.

11. The method of claim 10, further comprising formulating a new model when the model does not validate against the updated pattern.

12. The method of claim 11, wherein the recommended behavior includes two or more coupled metabolic effects.

13. A system comprising:

a display;

a processor in electrical communication with the display; 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,

validating the model against a subsample of the temporal data not used to formulate the model,

storing, to the memory, the model

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,

outputting the recommended behavior to a display associated with the computing device, and

receiving an indication the user accepted the recommendation.

14. The system of claim 13, wherein the model comprises a sigmoid form.

15. The system of claim 13, wherein receiving the temporal data includes receiving updated temporal data, and wherein the operations further comprise:

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

wherein validating the model includes validating the model against the updated pattern.

16. The system of claim 15, wherein the operations further comprise formulating a new model when the model does not validate against the updated pattern.

17. The system of claim 13, wherein the system is one of a blood glucose meter, an infusion pump, and a smartphone.