Healthcare model of wellness

• US 20050236004A1
• Filed: 03/25/2004
• Published: 10/27/2005
• Est. Priority Date: 03/25/2004
• Status: Abandoned Application

• Current Assignee: Magnus Labs, Inc.
• Original Assignee: Magnus Labs, Inc.
• Inventors: Magnuson, Timothy J., DcRouin, Edward E., Long, Richard F.
• Application Number:
  US10/808,644
• Publication Number:
  US 20050236004A1
• Time in Patent Office: Days
• US Class Current: 128/898
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• 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

Healthcare model of wellness. A method is disclosed for monitoring the wellness state of a given human body. Measurable parameters of the physiologic metabolism of the the given human body are first sensed and then an interpretation is made of interpreted parameters of the physiologic metabolism of the given human body. This interpretation is made through the interpretation of the human brain associated with the give human body. The sensed measured parameters and the determined interpreted parameters comprise an input vector. This input vector is processed through a model of the given human body that is trained on a training data set comprised of historical measured parameters of the physiologic metabolism of the given human body that are sensed over time in conjunction with historical interpreted parameters of the physiologic metabolism of the given human body. The input vector comprises less than the set of historical measured parameters and the set of historical interpreted parameters, the output of the model providing a prediction of wellness of the given human body.

Claims

1. A method for monitoring the wellness state of a given human body of a person, comprising the steps of:

sensing measurable physiologic parameters of the physiologic metabolism of the given human body;

determining perceived physiologic parameters of the physiologic metabolism of the given human body through interface with the human brain associated with the given human body, which perceived physiologic parameters are parameters relating to the physiologic metabolism of the given human body that can only be determined by interface of the human brain with the physiologic metabolism of the associated given human body;

wherein the sensed measured physiologic parameters and the determined perceived physiologic parameters comprise an input vector; and

processing the input vector through a model of the given human body that is trained on a training data set comprised of historical measured physiologic parameters of the physiologic metabolism of the given human body that are sensed over time in conjunction with historical perceived physiologic parameters of the physiologic metabolism of the given human body, wherein the input vector comprises less than the set of historical measured physiologic parameters and the set of historical perceived physiologic parameters, the output of the model providing a prediction of wellness of the given human body.

2. The method of claim 1, wherein the ratio of measured physiologic parameters in the input vector to the historical measured physiologic parameters is substantially greater than the ratio of the perceived physiologic parameters in the input vector to the historical perceived physiologic parameters.

3. The method of claim 1, wherein the interface to the human brain comprises an audible interface.

4. The method of claim 1, wherein the interface to the human brain comprises a tactile interface.

5. The method of claim 4, wherein the tactile interface comprises a written interface.

6. The method of claim 1, and further comprising the step of measuring external parameters that affect the physiologic metabolism of the given human body and the input vector includes the measured external parameters and the training data set includes historical external parameters.

7. The method of claim 6, wherein the external parameters include environmental parameters.

8. The method of claim 6, wherein the environmental parameters include environmental parameters from the group of relative humidity, pollen count, mold count, ambient temperature, air quality and barometric pressure.

9. The method of claim 1, wherein the model is a linear model.

10. The method of claim 1, wherein the model is a non-linear model.

11. The method of claim 10, wherein the non-linear model comprises a neural network.

12. The method of claim 1, wherein the measured physiologic parameters are selected from the group of blood pressure, body temperature, pulse, blood chemistry, pedometer count, and urine chemistry.

13. The method of claim 1, wherein the historical perceived physiologic parameters are collected by the steps of recording perceived parameters of the wellness of the given human body by the associated brain and recording such perceptions.

14. The method of claim 13, wherein the step of recording comprises responding to predetermined queries at predetermined times over a set time span.

15. The method of claim 1, wherein the model comprises a representation of the physiological metabolism of the given human body combined with the inherent learned behavior of the associated brain when making perceptions of the physiological metabolism of the given human body.

16. A method for determining sensitivities of the metabolism of the human body for an individual to their surrounding, comprising the steps of:

collecting metabolic data that is measurable of the state of the individual'"'"'s metabolism over a a determinable time period, which collected metabolic data comprises measurable variables of the metabolism associated with the human body of the individual;

collecting perceptions from the individual over the determinable time period about their perceived state of wellness, which collected perceptions comprise perceived variables;

the collected metabolic data and perceptions comprising historical data associated with that individual;

training a model on the historical data to model one or more parameters relating to the metabolism of the individual with select ones of the measured and perceived variables comprising inputs to the model and others thereof comprising outputs to the model; and

determining the sensitivity of the one or more parameters on which the trained model was trained on one or more of the perceived and measured variables that comprised inputs to the model over time.

17. The method of claim 16, wherein at least one of the measured variables comprises products ingested by the individual during the determinable time period.

22. The method of claim 16, wherein the measurable variables include external parameters that affect the physiologic metabolism of the human body of the individual over the determinable time period.

23. The method of claim 22, wherein the external parameters include environmental parameters.

24. The method of claim 22, wherein the environmental parameters include environmental parameters from the group of relative humidity, pollen count, mold count, ambient temperature, air quality and barometric pressure.

25. The method of claim 16, wherein the measured variables are selected from the group of blood pressure, body temperature, pulse, blood chemistry, pedometer count, and urine chemistry.

26. The method of claim 16, wherein the perception by the individual are collected by the steps of the individual recording perceived parameters as they personally perceive them of the wellness of their human body by the associated brain and recording such perceptions.

27. The method of claim 26, wherein the step of recording comprises responding to predetermined queries at predetermined times over the predetermined time period.

28. The method of claim 16, wherein the model comprises a representation of the physiological metabolism of the individual'"'"'s human body combined with the inherent learned behavior of the associated brain when making perceptions of the physiological metabolism of the individual'"'"'s human body.

18. The method of claim 18, wherein the products ingested are metabolized by the human body of the individual over the determinable time period in a known manner and the model is trained with the known manner that the ingested product is metabolized over the determinable time period as one of the inputs to the model, and wherein the sensitivity of one of the outputs of the model can be determined on the amount of the ingested product at the time of ingestion relative to the determinable period of time.

19. The method of claim 18, wherein the known manner can be determined for a generalized human body that is modeled on observations and measurements taken over a cross section of human bodies.

20. The method of claim 19, wherein the ingested product is modeled with a first principles model that models metabolism of the ingested product as a function of time and the amount of the ingested product.

21. The method of claim 18, wherein the known manner that the ingested product is metabolized is specific to the individual.