Synthetic Events For Real Time Patient Analysis

• US 20080294692A1
• Filed: 06/09/2008
• Published: 11/27/2008
• Est. Priority Date: 10/03/2006
• Status: Active Grant

• Current Assignee: ServiceNow Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Kraemer, James R., Angell, Robert Lee, Friedlander, Robert R.
• Granted Patent: US 8,145,582 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Generating synthetic events based on a vast amount of data. First and second data are received. The first data is organized into a first cohort. The second data is organized into a second cohort. The first cohort and the second cohort are processed to generate a synthetic event. The synthetic event comprises third data representing a result of a mathematical computation defined by S(p1)==>F(p2), wherein S comprises input facts with probability p1, wherein the input facts comprise the first cohort and the second cohort, and wherein F comprises an inferred event with probability p2. The term “event” means a particular set of data that represents, encodes, or records at least one of a thing or happening. Each of the first data, the second data, the first cohort, the second cohort, the synthetic event, and subcomponents thereof all comprise different events. The synthetic event is stored.

Claims

1. A computer implemented method comprising:

receiving a first set of data;

receiving a second set of data different than the first set of data;

organizing the first set of data into a first cohort;

organizing the second set of data into a second cohort;

processing the first cohort and the second cohort to generate a synthetic event, wherein the synthetic event comprises a third set of data representing a result of a mathematical computation defined by the operation S(p1)==>

F(p2), wherein S comprises a set of input facts with probability p1, wherein the set of input facts comprise the first cohort and the second cohort, wherein F comprises an inferred event with probability p2, wherein the term “

event”

means a particular set of data that represents, encodes, or records at least one of a thing or happening, and wherein each of the first set of data, the second set of data, the first cohort, the second cohort, the synthetic event, and subcomponents thereof all comprise different events; and

storing the synthetic event.

2. The computer implemented method of claim 1 wherein each corresponding event of the different events is represented as a corresponding pointer, and wherein each corresponding subcomponent of an event is represented as an additional corresponding pointer.

3. The computer implemented method of claim 2 wherein performing inference analysis comprises:

performing calculations regarding the first cohort using a first thread executing on a processor having multi-threading functionality; and

performing calculations regarding the second cohort using a second thread executing on the processor.

4. The computer implemented method of claim 1 wherein the first cohort comprises a plurality of data and the second cohort comprises a single datum.

5. The computer implemented method of claim 1 wherein the first cohort is derived from a first set of sub-cohorts and wherein the second cohort is derived from a second set of sub-cohorts.

6. The computer implemented method of claim 1 wherein directly comparing the first set of data to the second set of data would be computationally explosive.

7. The computer implemented method of claim 6 wherein the first set of data represents corresponding gene patterns of corresponding patients in a first set of humans, and wherein the second set of data represents gene patterns of a second set of humans.

8. The computer implemented method of claim 6 further comprising:

receiving a third set of data;

organizing the third set of data into a third cohort;

organizing the synthetic event into a fourth cohort;

processing the first cohort, the second cohort, the third cohort, and the fourth cohort to generate a second synthetic event; and

storing the second synthetic event.

9. The computer implemented method of claim 8 further comprising:

processing the first synthetic event and the second synthetic event to generate a third synthetic event; and

storing the third synthetic event.

10. The computer implemented method of claim 8 wherein the first set of data represents gene patterns of individual patients, the second set of data represents diet patterns of a population of individuals in a geographical location, the third set of data represents health records of the individual patients, the synthetic event represents a probability of that a sub-population of particular ethnic origin will develop cancer, and wherein the second synthetic event comprises a probability that the individual patients will develop cancer.

11. The computer implemented method of claim 10 further comprising:

processing the first synthetic event and the second synthetic event to generate a third synthetic event; and

storing the third synthetic event.

12. The computer implemented method of claim 11 wherein the third synthetic event comprises a probability that a specific patient in the individual patients will develop cancer.

13. A computer program product comprising:

a computer readable medium storing instructions for carrying out a computer implemented method, the instructions comprising;

instructions for receiving a first set of data;

instructions for receiving a second set of data different than the first set of data;

instructions for organizing the first set of data into a first cohort;

instructions for organizing the second set of data into a second cohort; and

instructions for processing the first cohort and the second cohort to generate a synthetic event, wherein the synthetic event comprises a third set of data representing a result of a mathematical computation defined by the operation S(p1)==>

F(p2), wherein S comprises a set of input facts with probability p1, wherein the set of input facts comprise the first cohort and the second cohort, wherein F comprises an inferred event with probability p2, wherein the term “

event”

means a particular set of data that represents, encodes, or records at least one of a thing or happening, and wherein each of the first set of data, the second set of data, the first cohort, the second cohort, the synthetic event, and subcomponents thereof all comprise different events.

14. The computer program product of claim 13 wherein each corresponding event of the different events is represented as a corresponding pointer, and wherein each corresponding subcomponent of an event is represented as an additional corresponding pointer.

15. The computer program product of claim 14 wherein the instructions for performing inference analysis comprises:

instructions for performing calculations regarding the first cohort using a first thread executing on a processor having multi-threading functionality; and

instructions for performing calculations regarding the second cohort using a second thread executing on the processor.

16. The computer program product of claim 13 wherein a direct comparison of the first set of data to the second set of data is computationally explosive.

17. A data processing system comprising:

a bus;

a processor connected to the bus, the processor having multi-threaded functionality;

a memory connected to the bus, the memory storing instructions for carrying out a computer implemented method, wherein the processor is capable of carrying out the instructions to;

receive a first set of data;

receive a second set of data different than the first set of data;

organize the first set of data into a first cohort;

organize the second set of data into a second cohort; and

process the first cohort and the second cohort to generate a synthetic event, wherein the synthetic event comprises a third set of data representing a result of a mathematical computation defined by the operation S(p1)==>

F(p2), wherein S comprises a set of input facts with probability p1, wherein the set of input facts comprise the first cohort and the second cohort, wherein F comprises an inferred event with probability p2, wherein the term “

event”

means a particular set of data that represents, encodes, or records at least one of a thing or happening, and wherein each of the first set of data, the second set of data, the first cohort, the second cohort, the synthetic event, and subcomponents thereof all comprise different events.

18. The data processing system of claim 17 wherein each corresponding event of the different events is represented as a corresponding pointer, and wherein each corresponding subcomponent of an event is represented as an additional corresponding pointer.

19. The data processing system of claim 18 wherein the processor is further capable of executing the set of instructions to:

perform calculations regarding the first cohort using a first thread executing on the processor; and

perform calculations regarding the second cohort using a second thread executing on the processor.

20. The data processing system of claim 17 wherein a direct comparison of the first set of data to the second set of data is computationally explosive.