Health Care Clinical Data Controlled Data Set Generator

• US 20200134063A1
• Filed: 10/30/2018
• Published: 04/30/2020
• Est. Priority Date: 10/30/2018
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Sexton, James A.
• Granted Patent: US 10,901,980 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a data set generator. A statistics gathering engine executing within the data set generator calculates statistics and metrics across multiple data dimensions for a plurality of clients and a plurality of each client'"'"'s data sources. The statistics gathering engine stores the statistics and metrics in a client-specific data map data structure for each client. An aggregated data map engine executing within the data set generator aggregates the statistics and metrics into an aggregated data map. The aggregated data map engine stores the aggregated data map in an aggregated data map data structure. The data set generator creates a client database construct. An insert engine executing within the data set generator populates the client database construct with new data based on the aggregated data map data structure.

Claims

1. A method, in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a data set generator, the method comprising:

calculating, by a statistics gathering engine executing within the data set generator, statistics and metrics across multiple data dimensions for a plurality of clients and a plurality of each client'"'"'s data sources;

storing, by the statistics gathering engine, the statistics and metrics in a client-specific data map data structure for each client;

aggregating, by an aggregated data map engine executing within the data set generator, the statistics and metrics into an aggregated data map;

storing, by the aggregated data map engine, the aggregated data map in an aggregated data map data structure;

creating, by the data set generator, a client database construct; and

populating, by an insert engine executing within the data set generator, the client database construct with new data based on the aggregated data map data structure.

2. The method of claim 1, wherein the multiple data dimensions comprise patient clinical demographics, patient logical demographics, patient clinical events, patient engagement events, provider demographics, and application end user and usage statistics data.

3. The method of claim 1, wherein aggregating the statistics and metrics comprises:

for a given data type, determining an aggregation model for the given data type;

obtaining raw data values for the given data type from all client-specific data map data structures; and

aggregating the raw data values for the given data type based on the aggregation model.

4. The method of claim 3, wherein aggregating the statistics and metrics further comprises:

responsive to the aggregation model being simple average, calculating a mean value from the raw data values and storing the mean value in the aggregated data map data structure.

5. The method of claim 3, wherein aggregating the statistics and metrics further comprises:

responsive to the aggregation model being simple append, getting all unique values from the raw data values and storing the unique values in the aggregated data map data structure.

6. The method of claim 3, wherein aggregating the statistics and metrics further comprises:

responsive to the aggregation model being normalized average;

determining a normalization type for the given data type;

performing a normalization process based on the normalization type to form normalized data values;

calculating a mean value from the normalized data values; and

storing the mean value in the aggregated data map data structure.

7. The method of claim 6, wherein the normalization type comprises discard outliers, zero-slope shift, or weight-shifted outliers.

8. The method of claim 3, wherein aggregating the statistics and metrics further comprises:

responsive to the aggregation model being normalized append;

determining a normalization type for the given data type;

performing a normalization process based on the normalization type to form normalized data values;

getting all unique values from the normalized data values; and

storing the unique values in the aggregated data map data structure.

9. The method of claim 8, wherein the normalization type comprises discard outliers, zero-slope shift, or weight-shifted outliers.

10. The method of claim 3, wherein aggregating the statistics and metrics further comprises:

responsive to the aggregation model being benchmark, calculating minimum and maximum values from the raw data values and storing the minimum and maximum values in the aggregated data map data structure.

11. A computer program product comprising a non-transitory computer readable medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to implement a data set generator, wherein the computer readable program causes the computing device to:

calculate, by a statistics gathering engine executing within the data set generator, statistics and metrics across multiple data dimensions for a plurality of clients and a plurality of each client'"'"'s data sources;

store, by the statistics gathering engine, the statistics and metrics in a client-specific data map data structure for each client;

aggregate, by an aggregated data map engine executing within the data set generator, the statistics and metrics into an aggregated data map;

store, by the aggregated data map engine, the aggregated data map in an aggregated data map data structure;

create, by the data set generator, a client database construct; and

populate, by an insert engine executing within the data set generator, the client database construct with new data based on the aggregated data map data structure.

12. The computer program product of claim 11, wherein aggregating the statistics and metrics comprises:

for a given data type, determining an aggregation model for the given data type;

obtaining raw data values for the given data type from all client-specific data map data structures; and

aggregating the raw data values for the given data type based on the aggregation model.

13. The computer program product of claim 12, wherein aggregating the statistics and metrics further comprises:

responsive to the aggregation model being simple average, calculating a mean value from the raw data values and storing the mean value in the aggregated data map data structure.

14. The computer program product of claim 12, wherein aggregating the statistics and metrics further comprises:

responsive to the aggregation model being simple append, getting all unique values from the raw data values and storing the unique values in the aggregated data map data structure.

15. The computer program product of claim 12, wherein aggregating the statistics and metrics further comprises:

responsive to the aggregation model being normalized average;

determining a normalization type for the given data type;

performing a normalization process based on the normalization type to form normalized data values;

calculating a mean value from the normalized data values; and

storing the mean value in the aggregated data map data structure.

16. The computer program product of claim 15, wherein the normalization type comprises discard outliers, zero-slope shift, or weight-shifted outliers.

17. The computer program product of claim 12, wherein aggregating the statistics and metrics further comprises:

responsive to the aggregation model being normalized append;

determining a normalization type for the given data type;

performing a normalization process based on the normalization type to form normalized data values;

getting all unique values from the normalized data values; and

storing the unique values in the aggregated data map data structure.

18. The computer program product of claim 17, wherein the normalization type comprises discard outliers, zero-slope shift, or weight-shifted outliers.

19. The computer program product of claim 12, wherein aggregating the statistics and metrics further comprises:

responsive to the aggregation model being benchmark, calculating minimum and maximum values from the raw data values and storing the minimum and maximum values in the aggregated data map data structure.

20. An apparatus, comprising:

a processor; and

a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to implement a data set generator, wherein the instructions cause the processor to;

calculate, by a statistics gathering engine executing within the data set generator, statistics and metrics across multiple data dimensions for a plurality of clients and a plurality of each client'"'"'s data sources;

store, by the statistics gathering engine, the statistics and metrics in a client-specific data map data structure for each client;

aggregate, by an aggregated data map engine executing within the data set generator, the statistics and metrics into an aggregated data map;

store, by the aggregated data map engine, the aggregated data map in an aggregated data map data structure;

create, by the data set generator, a client database construct; and

populate, by an insert engine executing within the data set generator, the client database construct with new data based on the aggregated data map data structure.