Method and System for Ontology Based Analytics

• US 20130096944A1
• Filed: 10/13/2011
• Published: 04/18/2013
• Est. Priority Date: 10/13/2011
• Status: Abandoned Application

• Current Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford University)
• Original Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford University)
• Inventors: Shah, Nigam, Musen, Mark A.
• Application Number:
  US13/273,038
• Publication Number:
  US 20130096944A1
• Time in Patent Office: Days
• US Class Current: 705/3
• 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
• CPC Class Codes: G16H 70/60 relating to pathologies

Abstract

The present invention provides a mechanism to use terminologies and ontologies for the purpose of indexing, annotating and semantically marking up existing collections of datasets. The invention further provides a system for incorporating terminologies, ontologies, and contextual annotation in specific domains, such as utilizing biomedical concept hierarchies in data analytics. The resulting rich structure supports specific mechanisms for data mining and machine learning.

Claims

1. A method for analyzing digital medical records, comprising:

receiving a graph structure for a first state of interest;

receiving a graph structure for a second state of interest;

developing a dictionary of terms of interest;

mapping the dictionary of terms to the graph structures;

receiving at least one digital medical record;

recognizing terms from the dictionary of terms of interest that are contained within the at least one digital medical record;

generating statistics for the recognized terms.

2. The method of claim 1, wherein the digital medical records include coded information and further comprising generating statistics for the coded information.

3. The method of claim 1, further comprising recognizing negated terms from the dictionary of terms of interest that are contained within the at least one digital medical record.

4. The method of claim 3, further comprising generating statistics for the recognized negated terms.

5. The method of claim 1, wherein the generated statistics include correlations between two terms from the dictionary of terms of interest.

6. The method of claim 1, wherein the generated statistic is a reporting odds ratio for at least two terms from the dictionary of terms of interest.

7. The method of claim 1, wherein the generated statistic is a proportional reporting ratio for at least two terms from the dictionary of terms of interest.

8. The method of claim 1, wherein the first state of interest is a disease and a second state of interest is a drug.

9. The method of claim 1, wherein the generated statistic is associated with a point in the graph structure for a first state of interest and a point in the graph structure for the second state of interest.

10. The method of claim 1 wherein the recognized terms from the dictionary of terms includes a temporal indication.

11. A computer-readable medium including instructions that, when executed by a processing unit, cause the processing unit to analyzing digital medical records, by performing the steps of:

receiving a graph structure for a first state of interest;

receiving a graph structure for a second state of interest;

developing a dictionary of terms of interest;

mapping the dictionary of terms to the graph structures;

receiving at least one digital medical record;

recognizing terms from the dictionary of terms of interest that are contained within the at least one digital medical record;

generating statistics for the recognized terms.

12. The computer-readable medium of claim 11, wherein the digital medical records include coded information and further comprising generating statistics for the coded information.

13. The computer-readable medium of claim 11, further comprising recognizing negated terms from the dictionary of terms of interest that are contained within the at least one digital medical record.

14. The computer-readable medium of claim 13, further comprising generating statistics for the recognized negated terms.

15. The computer-readable medium of claim 11, wherein the generated statistics include correlations between two terms from the dictionary of terms of interest.

16. The computer-readable medium of claim 11, wherein the generated statistic is a reporting odds ratio for at least two terms from the dictionary of terms of interest.

17. The computer-readable medium of claim 11, wherein the generated statistic is a proportional reporting ratio for at least two terms from the dictionary of terms of interest.

18. The computer-readable medium of claim 11, wherein the first state of interest is a disease and a second state of interest is a drug.

19. The computer-readable medium of claim 11, wherein the generated statistic is associated with a point in the graph structure for a first state of interest and a point in the graph structure for the second state of interest.

20. The computer-readable medium of claim 11 wherein the recognized terms from the dictionary of terms includes a temporal indication.

21. A computing device comprising:

a data bus;

a memory unit coupled to the data bus;

a processing unit coupled to the data bus and configured to receive a graph structure for a first state of interest;

receive a graph structure for a second state of interest;

develop a dictionary of terms of interest;

map the dictionary of terms to the graph structures;

receive at least one digital medical record;

recognize terms from the dictionary of terms of interest that are contained within the at least one digital medical record;

generate statistics for the recognized terms.