Medical risk stratifying method and system

• US 7,487,134 B2
• Filed: 10/25/2005
• Issued: 02/03/2009
• Est. Priority Date: 10/25/2005
• Status: Expired due to Fees

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Grichnik, Anthony J., Seskin, Michael
• Primary Examiner(s): Vincent; David R
• Assistant Examiner(s): STARKS, WILBERT L
• Application Number:
  US11/257,341
• Publication Number:
  US 20070179769A1
• Time in Patent Office: 1,197 Days
• Field of Search: 706/60
• US Class Current: 706/60
• 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 computer-implemented 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;

presenting the values of the plurality of medical risks;

optimizing the plurality of health parameters to minimize the plurality of medical risks simultaneously based upon at least desired values of the plurality of medical risks and sensitivity of the plurality of medical risks to the plurality of health parameters using a zeta statistic measure; and

re-presenting the values of the plurality of medical risks.

2. The computer-implemented method according to claim 1, wherein the establishing includes:

obtaining data records associated with a plurality of health variables and the plurality of medical risks;

selecting the plurality of health parameters from the plurality of health variables, wherein a total number of the plurality of health parameters is less than a total number of the plurality of health variables; and

generating the medical risk process model indicative of the interrelationships; and

the optimizing includes;

determining desired statistical distributions of the plurality of health parameters of the medical risk process model based upon at least the desired values of the plurality of medical risks and the sensitivity of the plurality of medical risks to the plurality of health parameters using the zeta statistic measure; and

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

3. The computer-implemented method according to claim 2, wherein selecting further includes:

pre-processing the data records; and

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

4. The computer-implemented method according to claim 2, wherein generating further includes:

creating the medical risk process model as 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.

5. The computer-implemented method according to claim 2, 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;

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

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 medical risk process model.

6. The computer-implemented 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 health information about the individual user.

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

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

8. The computer-implemented 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.

9. The computer-implemented method according to claim 8, further includes:

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

10. A computer system, comprising:

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

a processor that performs;

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

presenting the values of the plurality of medical risks;

optimizing the plurality of health parameters to minimize the plurality of medical risks simultaneously based upon at least desired values of the plurality of medical risks and sensitivity of the plurality of medical risks to the plurality of health parameters using a zeta statistic measure; and

re-presenting the values of the plurality of medical risks.

11. The computer system according to claim 10, wherein establishing the medical risk process model further includes:

obtaining data records associated with a plurality of health variables and the plurality of medical risks;

selecting the plurality of health parameters from the plurality of health variables, wherein a total number of the plurality of health parameters is less than a total number of the plurality of health variables; and

generating the medical risk process model indicative of the interrelationships; and

optimizing the plurality of health parameters further includes;

determining desired statistical distributions of the plurality of health parameters of the medical risk process model based upon at least the desired values of the plurality of medical risks and the sensitivity of the plurality of medical risks to the plurality of health parameters using the zeta statistic measure; and

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

12. The computer system according, to claim 11, wherein selecting the plurality of health parameters further includes:

pre-processing the data records; and

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

13. The computer system according to claim 11, wherein generating the medical risk process model further includes:

creating the medical risk process model as 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.

14. The computer system according to claim 11, wherein determine the respective desired statistical distributions further includes

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;

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

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 medical risk process model.

15. The computer system according to claim 10, further including:

one or more input devices that obtains the set of values corresponding to the plurality of health parameters indicative of health information about an individual user or a population.

16. The computer system according to claim 10, further including:

a display device that performs;

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

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

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

17. A computer-readable medium for use on a computer system that performs 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;

presenting the values of the plurality of medical risks;

optimizing the plurality of health parameters to minimize the plurality of medical risks simultaneously based upon at least desired values of the plurality of medical risks and sensitivity of the plurality of medical risks to the plurality of health parameters using a zeta statistic measure; and

re-presenting the values of the plurality of medical risks.

18. The computer-readable medium according to claim 17, wherein

the establishing includes:

obtaining data records associated with a plurality of health variables and the plurality of medical risks;

selecting the plurality of health parameters from the plurality of health variables, wherein a total number of the plurality of health parameters is less than a total number of the plurality of health variables; and

generating medical risk process model indicative of the interrelationships; and

the optimizing includes;

determining desired statistical distributions of the plurality of health parameters of the medical risk process model based upon at least the desired values of the plurality of medical risks and the sensitivity of the plurality of medical risks to the plurality of health parameters using a zeta statistic measure; and

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

19. The computer-readable medium according to claim 18, wherein selecting further includes:

pre-processing the data records; and

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

20. The computer-readable medium according to claim 18, wherein generating further includes:

creating the medical risk process model as 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.

21. The computer-readable medium according to claim 18, 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;

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

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 medical risk process model.