SYSTEM, METHOD AND COMPUTER PROGRAM FOR MULTI-DIMENSIONAL TEMPORAL AND RELATIVE DATA MINING FRAMEWORK, ANALYSIS & SUB-GROUPING

• US 20140358926A1
• Filed: 12/12/2012
• Published: 12/04/2014
• Est. Priority Date: 12/12/2011
• Status: Active Grant

• Current Assignee: University Of Ontario Institute Of Technology
• Original Assignee: University Of Ontario Institute Of Technology
• Inventors: McGregor, Carolyn Patricia, Smith, Kathleen Patricia, Dhanoa, Agam
• Granted Patent: US 9,898,513 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/24568 Data stream processing; Continuous queries
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/2477 Temporal data queries
• CPC Class Codes: G06F 2216/03 Data mining
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/67 for remote operation
• 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

Abstract

The present invention relates to a system, method and computer program product that is a multi-dimensional data mining environment and that operable to apply a series of temporal and relative rules (i.e., STDMⁿ0) and is further operable in at least one of the following ways: to incorporate a framework to support temporal abstractions and relative alignments to data (i.e., STDMⁿ0); and to derive characteristics within the data (STDMⁿ0). The present invention may incorporate data from multiple sources, and potentially multiple centres. The analysis and alignment of the data may involve both temporal dimensions and other dimensions (or relative aspects) of the data. The present invention may further be a data mining environment that is flexible enough to permit relatively open ended queries thereby enabling, for example, the detection of trends, including trends with new dimensions, or trends based on relatively small data sets.

Claims

1. A computer implemented data mining method for mining data streams from multiple sites is provided, wherein different attributes may be associated with data streams, characterized by:

(a) using a central distribution computer system component to store

(i) a series of temporal rules and

(ii) relative rules for relatively aligning multi-dimensional data based on at least one time point of interest, the central distribution computer system when executed determining particular temporal rules applicable to data associated to a particular site, based on the different attributes;

(b) collecting at the multiple sites, and optionally cleaning, multi-dimensional data, the multi-dimensional data including a plurality of data streams;

(c) temporally abstracting the multi-dimensional data by accessing and applying the applicable temporal rules so as to generate temporally abstracted multi-dimensional data, and relatively aligning the temporally abstracted multi-dimensional data based on an at least one time point of interest by accessing and applying the applicable relative rules; and

(d) collecting temporally abstracted and relatively aligned data from the multiple sites to provide multi-dimensional, temporal, multi-site data for use in data mining operations.

2. The method of claim 1, comprising managing the distribution and application of the temporal rules and the relative rules across the multiple sites in a way that supports data mining operations across the multiple sites in real time or near real time.

3. The method of claim 1, wherein the different attributes may include one or more of:

(a) data structure, (b) data collection frequency, or (c) the type of device collecting the data (including manufacturer/model, approach of device to data correction or mechanism used for identifying artefacts in signals).

4. The method of claim 3 comprising distributing applicable temporal rules and applicable relative rules based on the attributes associated with the relevant data streams.

5. The method of claim 4, wherein each data stream relates to a human subject, and wherein the central distribution computer system when executed (a) initiates creation of simple abstractions for each human subject, and storage of the simple abstractions locally at each site, and tagging of the data streams using site identification data, and (b) initiates creation of complex abstractions using the applicable temporal rules and tagging of the complex abstractions with tagging information defined by the central distribution computer system so as to enable access for multi-site data mining operations initiated by the central distribution computer system.

6. The method of claim 1, wherein the multi-dimensional data is associated with two or more sites, and optionally is generated by two or more types of devices, and further optionally is associated with two or more research studies.

7. The method of claim 5 comprising generation of patient monitoring data in real time or near real time for use in connection with one or more patient care systems or patient monitoring systems.

8. The method of claim 5, wherein each data stream is associated with a particular human subject, and comprising dynamically defining groups or sub-groups of human subjects, or characteristics associated with such groups or sub-groups, and enabling data mining operations in real time or near real time based on such groups or sub-groups.

9. The method of claim 1, comprising the use of the results of the data mining operations to perform multi-site research data operations.

10. The method of claim 1, wherein the data includes physiological data collected by medical devices, wherein the data structure and/or frequency of data collected by the medical devices varies.

11. The method of claim 2, comprising storing the temporal rules and the relative rules in a data store that includes a hierarchy based on simple rules to complex rules.

12. The method of claim 1, wherein at least one data mining operation is based on null hypothesis testing.

13. A data mining computer system for mining data from multiple sites is provided, wherein different attributes may be associated with data streams:

(a) a central distribution computer system component to store

(i) a series of temporal rules and

(ii) relative rules for relatively aligning mufti-dimensional data based on at least one time point of interest, the central distribution computer system when executed determining particular temporal rules applicable to data associated to a particular site;

(b) one or more devices associated with two or more sites, the devices collecting data in a plurality of data streams; and

(c) at least one local computer at each site connected to cents al distribution computer system;

wherein;

the central distribution computer system when executed manages the temporal abstraction and relative alignment of the data streams so as to support data mining operations for multi-dimensional data across the multiple sites by;

accessing from the local computer information regarding the different attributes for the data streams;

providing to the local computer the applicable temporal rules end applicable relative rules thereby enabling temporal abstraction of the multi-dimensional data so as to generate temporally abstracted multi-dimensional date, and relative alignment of the temporally abstracted multi-dimensional data based on an at least one time point of interest in a way that addresses the different attributes; and

collecting the temporally abstracted and relatively aligned data from the multiple sites by communicating with the local computers and initiating the retrieval and transfer of the temporally abstracted and relatively aligned data based on a data mining request.

14. The computer system of claim 13, wherein the computer system when executed manages distribution and application of the temporal rules and the relative rules across the multiple sites in a way that supports data mining operations across the multiple sites in real time or near real time.

15. The computer system of claim 13, wherein the different attributes may include one or more of:

(a) data structure, (b) data collection frequency, or (c) the type of device collecting the data (including manufacturer/model, approach of device to data correction or mechanism for identifying artefacts in signals).

16. The computer system of claim 15, wherein the computer system when executed distributes applicable temporal rules and applicable relative rules based on the attributes associated with the relevant data streams.

17. The computer system of claim 16, wherein each data stream relates to a human subject, and wherein the central distribution computer system when executed (a) initiates creation of simple abstractions for each human subject, and storage of the simple abstractions locally at each site, and tagging of the data streams using site identification data, and (b) initiates creation of complex abstractions using the applicable temporal rules and tagging of the complex abstractions with tagging information defined by the central distribution computer system so as to enable access for multi-site data mining operations initiated by the central distribution computer system.

18. The computer system of claim 15, wherein when executed the computer system generates patient monitoring data in real time or near real time for use in connection with one or more patient care systems or patient monitoring systems.

19. The computer system of claim 18, wherein each data stream is associated with a particular human subject, and when executed the computer system dynamically defines groups or sub-groups of human subjects, or characteristics associated with such groups or sub-groups, and thereby permits data mining operations in real time or near real time based on such groups or sub-groups.