Data driven predictive analysis of complex data sets for determining decision outcomes

• US 9,679,028 B1
• Filed: 09/19/2016
• Issued: 06/13/2017
• Est. Priority Date: 09/19/2016
• Status: Active Grant

• Current Assignee: Included Health, Inc.
• Original Assignee: Grand Rounds, Inc.
• Inventors: Sanghvi, Jayodita, Sharp, Robert, Freese, Nathaniel Sayer
• Primary Examiner(s): Jacob, Ajith
• Application Number:
  US15/269,888
• Time in Patent Office: 267 Days
• CPC Class Codes: G06F 16/244 Grouping and aggregation
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/20 relating to practices or guidelines

Abstract

Systems and methods are provided for data driven predictive analysis of complex data sets for determining decision outcomes. The systems and methods include obtaining a first set of data associated with individuals and obtaining, a second set of data associated with events, wherein the events are associated with at least one of the individuals. The systems and methods further include determining a subset of the individuals from the first set of data based on the first subset of individuals having common attributes with a target individual and determining a second subset of events from the second set of data based on the second subset of events having common attributes with the target events associated with the target individual. Additionally, the systems and methods include aggregating data associated with the second subset of events based on the target events and providing for display output associated with aggregated data.

Claims

1. A non-transitory computer readable storage medium storing instructions that are executable by a first computing device that includes one or more processors to cause the first computing device to perform a method comprising:

obtaining, from one or more data sources, a first set of data associated with a plurality of individuals;

obtaining, from one or more data sources, a second set of data associated with a plurality of events, wherein the events are associated with at least one of the plurality of individuals;

acquiring information associated with a target individual and one or more target events;

determining a first subset of one or more individuals from the first set of data based on a the first subset of one or more individuals having a first set of one or more common attributes with a target individual;

determining a second subset of one or more events from the second set of data based on the second subset of one or more events having a second set of one or more common attributes with one or more target events associated with the target individual;

aggregating data associated with the second subset of one or more events based on the one or more target events; and

providing for display output associated with aggregated data.

2. The non-transitory computer readable storage medium of claim 1, wherein the first set one or more common attributes are selected based on the target events.

3. The non-transitory computer readable storage medium of claim 1, wherein the second set of one or more common attributes are selected based on the target events.

4. The non-transitory computer readable storage medium of claim 1, wherein aggregating the data from the second subset of one or more events includes applying statistical operations to the data.

5. The non-transitory computer readable storage medium of claim 3, wherein the statistical operations applied to the data generate a weight value associated with each of the target events.

6. The non-transitory computer readable storage medium of claim 1, wherein the output includes a representation of the aggregation.

7. The non-transitory computer readable storage medium of claim 1, wherein the output includes a representation of the data used in the aggregation.

8. The non-transitory computer readable storage medium of claim 1, wherein the target individual and target events are provided by a user.

9. The non-transitory computer readable storage medium of claim 1 wherein one or more attributes of the first set of data and one or more attributes of the second set of data are generated based on information from more than one data source.

10. An electronic device comprising:

one or more data storages configured to store one or more data sets associated with a plurality of events and one or more data sources associated with a plurality of individuals;

a matching engine configured to;

determine a first subset of one or more individuals from the one or more data sets associated with a plurality of individuals based on a the first subset of one or more individuals having one or more common attributes with a target individual;

determine a second subset of one or more events from the one or more data sets associated with a plurality of events having one or more common attributes with one or more target events associated with the target individual; and

an analysis engine configured to;

aggregate data associated with the second subset of the one or more events based on the one or more target events; and

provide for output data representing the aggregated data.

11. The electronic device of claim 10, wherein the one or more common attributes of the one or more data sets associated with a plurality of events and the one or more target events are common attributes that are relevant, and wherein the relevancy of the one or more common attributes is based on the one or more target events.

12. The electronic device of claim 10, wherein the aggregate data is generated by applying statistical operations to the data associated with the second subset of the one or more events.

13. The electronic device of claim 12, wherein the statistical operations applied to the data generate a weight value associated with each of the target events.

14. The electronic device of claim 10, wherein the output includes a representation of the aggregate data.

15. The electronic device of claim 10, wherein:

the one or more data storages are further configure to store one or more attributes associated with the plurality of events or the plurality of individuals wherein the one or more attributes are generated based on data in at least one of the one or more data sets associated with individuals and the one or more data sets associated with events.

16. A method performed by one or more processors and comprising:

obtaining, from one or more data sources, a first set of data associated with a plurality of individuals;

obtaining, from one or more data sources, a second set of data associated with a plurality of events, wherein the events are associated with at least one of the plurality of individuals;

determining a first subset of one or more individuals from the first set of data based on a the first subset of one or more individuals having one or more common attributes with a target individual;

determining a second subset of one or more events from the second set of data based on the second subset of one or more events having one or more common attributes with one or more target events associated with the target individual;

aggregating data associated with the second subset of one or more events based on the one or more target events; and

providing for display output associated with aggregated data.

17. The method of claim 16, wherein the one or more common attributes of the one or more events and the one or more target events are common attributes that are relevant, and wherein the relevancy of the one or more common attributes is based on the one or more target events.

18. The method of claim 16, wherein aggregating the data from the second subset of one or more events includes applying statistical operations to the data.

19. The method of claim 18, wherein the statistical operations applied to the data generate a weight value associated with each of the target events.

20. The method of claim 16, wherein the output includes a representation of the aggregation.