System and method to optimize control cohorts using clustering algorithms

• US 20080082356A1
• Filed: 10/03/2006
• Published: 04/03/2008
• Est. Priority Date: 10/03/2006
• Status: Active Grant

• Current Assignee: LinkedIn Corporation (Microsoft Corporation)
• Original Assignee: International Business Machines Corporation
• Inventors: Hennessy, Richard A., Kraemer, James R., Friedlander, Robert R., Rollins, John Baxter
• Granted Patent: US 7,809,660 B2
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/00 ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms

Abstract

A computer implemented method, apparatus, and computer usable program code for automatically selecting an optimal control cohort. Attributes are selected based on patient data. Treatment cohort records are clustered to form clustered treatment cohorts. Control cohort records are scored to form potential control cohort members. The optimal control cohort is selected by minimizing differences between the potential control cohort members and the clustered treatment cohorts.

Claims

1. A computer implemented method for automatically selecting an optimal control cohort, the computer implemented method comprising:

selecting attributes based on patient data;

clustering of treatment cohort records to form clustered treatment cohorts;

scoring control cohort records to form potential control cohort members; and

selecting the optimal control cohort by minimizing differences between the potential control cohorts members and the clustered treatment cohorts.

2. The computer implemented method of claim 1, wherein the patient data is stored in a clinical database.

3. The computer implemented method of claim 1, wherein the attributes are any of features, variables, and characteristics.

4. The computer implemented method of claim 1, wherein the selecting step further comprises:

searching the patient data to determine the attributes that most strongly differentiate assignment of patient records to particular clusters.

5. The computer implemented method of claim 1, wherein the clustered treatment cohorts show a number of clusters and characteristics of each of the number of clusters.

6. The computer implemented method of claim 1, wherein the attributes include gender, age, disease state, genetics, and physical condition.

7. The computer implemented method of claim 1, wherein each patient record is scored to calculate the Euclidean distance to all clusters.

8. The computer implemented method of claim 5, wherein a user specifies the number of clusters for the clustered treatment cohorts and a number of search passes through the patient data to generate the number of clusters.

9. The computer implemented method of claim 1, wherein the selecting attributes and the clustering steps are performed by a data mining application, wherein the selecting the optimal control cohort step is performed by a 0-1 integer programming model.

10. The computer implemented method of claim 1, wherein the scoring step comprises:

scoring all patient records by computing a Euclidean distance to cluster prototypes of all treatment cohorts.

11. The computer implemented method of claim 1, further comprising:

providing names, unique identifiers, or encoded indices of individuals in the optimal control cohort.

12. The computer implemented method of claim 1, wherein the clustering step further comprise:

generating a feature map to form the clustered treatment cohorts.

13. The computer implemented method of claim 12, wherein the feature map is a Kohonen feature map.

14. An optimal control cohort selection system comprising:

an attribute database operatively connected to a clinical information system for storing patient records including attributes of patients;

a server operably connected to the attribute database wherein the server executes a data mining application and a clinical control cohort selection program wherein the data mining application selects specified attributes based on patient data, clusters treatment cohort records based on the specified attributes to form clustered treatment cohorts, and clusters control cohort records based on the specified attributes to form clustered control cohorts; and

wherein the clinical control cohort selection program selects the optimal control cohort by minimizing differences between the clustered control cohorts and the clustered treatment cohorts.

15. The control cohort selection system of claim 14, wherein the clinical information system includes information about populations of patients wherein the information is accessed by the server.

16. The control cohort selection system of claim 14, wherein the data mining application is IBM DB2 Intelligent Miner.

17. A computer program product comprising a computer usable medium including computer usable program code for automatically selecting an optimal control cohort, the computer program product comprising:

computer usable program code for selecting attributes based on patient data;

computer usable program code for clustering of treatment cohort records to form clustered treatment cohorts;

computer usable program code for scoring control cohort records to form potential control cohort members; and

computer usable program code for selecting the optimal control cohort by minimizing differences between the potential control cohorts members and the clustered treatment cohorts.

18. The computer program product of claim 17, further comprising:

computer usable program code for scoring all patient records in a self organizing map by computing a Euclidean distance to cluster prototypes of all treatment cohorts; and

computer usable program code for generating a feature map to form the clustered treatment cohorts.

19. The computer program product of claim 17, comprising computer usable program code for specifying a number of clusters for the clustered treatment cohorts and a number of search passes through the patient data to generate the number of clusters.

20. The computer program product of claim 17, wherein the computer usable program code for selecting further comprises:

computer usable program code for searching the patient data to determine the attributes that most strongly differentiate assignment of patient records to particular clusters.