Medical risk stratifying method and system

• US 20070179769A1
• Filed: 10/25/2005
• Published: 08/02/2007
• Est. Priority Date: 10/25/2005
• Status: Active Grant

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Seskin, Michael, Grichnik, Anthony
• Granted Patent: US 7,487,134 B2
• Time in Patent Office: Days
• US Class Current: 703/11
• 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: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A method is provided for a medical risk stratification system. The method may include establishing a medical risk process model indicative of interrelationships between a plurality of medical risks and a plurality of health parameters and obtaining a set of values corresponding to the plurality of health parameters. The method may also include calculating the values of the plurality of medical risks simultaneously based upon the set of values corresponding to the plurality of health parameters and the medical risk process model and presenting the values of the plurality of medical risks.

Claims

1. A method for a medical risk stratification system, comprising:

establishing a medical risk process model indicative of interrelationships between a plurality of medical risks and a plurality of health parameters;

obtaining a set of values corresponding to the plurality of health parameters;

calculating the values of the plurality of medical risks simultaneously based upon the set of values corresponding to the plurality of health parameters and the medical risk process model; and

presenting the values of the plurality of medical risks.

2. The method according to claim 1, further including:

optimizing the plurality of health parameters to minimize the plurality of medical risks simultaneously.

3. The method according to claim 1, wherein the establishing includes:

obtaining data records associated one or more health variables and the plurality of medical risks;

selecting the plurality of health parameters from the one or more health variables;

generating a computational model indicative of the interrelationships;

determining desired statistical distributions of the plurality of health parameters of the computational model; and

recalibrating the plurality of health parameters based on the desired statistical distributions.

4. The method according to claim 3, wherein selecting further includes:

pre-processing the data records; and

using a genetic algorithm to select the plurality of health parameters from the one or more health variables based on a mahalanobis distance between a normal data set and an abnormal data set of the data records.

5. The method according to claim 3, wherein generating further includes:

creating a neural network computational model;

training the neural network computational model using the data records; and

validating the neural network computation model using the data records.

6. The method according to claim 3, wherein determining further includes:

determining a candidate set of the health parameters with a maximum zeta statistic using a genetic algorithm; and

determining the desired distributions of the health parameters based on the candidate set,

wherein the zeta statistic ζ

is represented by;

$\mathrm{\zeta <br><br>}=\underset{1}{\overset{j}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{1}{\overset{i}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that x_i represents a mean of an ith input;

x_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| represents sensitivity of the jth output to the ith input of the computational model.

7. The method according to claim 1, wherein the obtaining includes:

obtaining, from an individual user of the medical risk stratification system, the set of values indicative of factual health information about the individual user.

8. The method according to claim 1, wherein the obtaining includes:

obtaining, from an individual user of the medical risk stratification system, the set of values indicative of desired health information about the individual user.

9. The method according to claim 1, wherein the obtaining includes:

obtaining the set of values indicative of factual and desired health information about a population.

10. The method according to claim 1, wherein the presenting includes:

presenting the set of values of the plurality of health parameters in a first display area;

presenting statistic data corresponding to the plurality of heath parameters in a second display area; and

presenting the interrelationships between the plurality of health parameters and the plurality of medical risks in a third display area.

11. The method according to claim 10, further includes:

presenting detailed data associated with the medical risk process model in a fourth display area.

12. A computer system, comprising:

a database containing data records associating a plurality of medical risks and a plurality of health parameters; and

a processor configured to;

establish a medical risk process model indicative of interrelationships between the plurality of medical risks and the plurality of health parameters;

obtain a set of values corresponding to the plurality of health parameters;

calculate the values of the plurality of medical risks simultaneously based upon the set of values corresponding to the plurality of health parameters and the medical risk process model;

present the values of the plurality of medical risks; and

optimize the plurality of health parameters to minimize the plurality of medical risks simultaneously.

13. The computer system according to claim 12, wherein, to establish the medical risk process model, the processor is further configured to:

obtain data records associated one or more health variables and the plurality of medical risks;

select the plurality of health parameters from the one or more health variables;

generate a computational model indicative of the interrelationships;

determine desired statistical distributions of the plurality of health parameters of the computational model; and

recalibrate the plurality of health parameters based on the desired statistical distributions.

14. The computer system according to claim 13, wherein, to select the plurality of health parameters, the processor is further configured to:

pre-process the data records; and

use a genetic algorithm to select the plurality of health parameters from the one or more health variables based on a mahalanobis distance between a normal data set and an abnormal data set of the data records.

15. The computer system according to claim 13, wherein, to generate the computational model, the processor is further configured to:

create a neural network computational model;

train the neural network computational model using the data records; and

validate the neural network computation model using the data records.

16. The computer system according to claim 13, wherein, to determine the respective desired statistical distributions, the processor is further configured to:

determine a candidate set of the health parameters with a maximum zeta statistic using a genetic algorithm; and

determine the desired distributions of the health parameters based on the candidate set,

wherein the zeta statistic ζ

is represented by;

$\mathrm{\zeta <br><br>}=\underset{1}{\overset{j}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{1}{\overset{i}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that x_i represents a mean of an ith input;

x_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| represents sensitivity of the jth output to the ith input of the computational model.

17. The computer system according to claim 12, further includes:

one or more input devices configured to obtain the set of values corresponding to the plurality of health parameters indicative of factual and desired health information about an individual user or a population.

18. The computer system according to claim 12, further includes:

a display device configured to;

display the set of values of the plurality of health parameters in a first display area;

display statistic data corresponding to the plurality of heath parameters in a second display area; and

display the interrelationships between the plurality of health parameters and the plurality of medical risks in a third display area.

19. A computer-readable medium for use on a computer system configured to perform a medical risk stratification procedure, the computer-readable medium having computer-executable instructions for performing a method comprising:

establishing a medical risk process model indicative of interrelationships between a plurality of medical risks and a plurality of health parameters;

obtaining a set of values corresponding to the plurality of health parameters;

calculating the values of the plurality of medical risks simultaneously based upon the set of values corresponding to the plurality of health parameters and the medical risk process model; and

presenting the values of the plurality of medical risks.

20. The computer-readable medium according to claim 19, wherein the method further includes:

optimizing the plurality of health parameters to minimize the plurality of medical risks simultaneously.

21. The computer-readable medium according to claim 19, wherein the establishing includes:

obtaining data records associated one or more health variables and the plurality of medical risks;

selecting the plurality of health parameters from the one or more health variables;

generating a computational model indicative of the interrelationships;

determining desired statistical distributions of the plurality of health parameters of the computational model; and

recalibrating the plurality of health parameters based on the desired statistical distributions.

22. The computer-readable medium according to claim 21, wherein selecting further includes:

pre-processing the data records; and

using a genetic algorithm to select the plurality of health parameters from the one or more health variables based on a mahalanobis distance between a normal data set and an abnormal data set of the data records.

23. The computer-readable medium according to claim 21, wherein generating further includes:

creating a neural network computational model;

training the neural network computational model using the data records; and

validating the neural network computation model using the data records.

24. The computer-readable medium according to claim 21, wherein determining further includes:

determining a candidate set of the health parameters with a maximum zeta statistic using a genetic algorithm; and

determining the desired distributions of the health parameters based on the candidate set,

wherein the zeta statistic ζ

is represented by;

$\mathrm{\zeta <br><br>}=\underset{1}{\overset{j}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{1}{\overset{i}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that x_i represents a mean of an ith input;

x_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| represents sensitivity of the jth output to the ith input of the computational model.