Ontology mapper

• US 10,268,687 B1
• Filed: 12/28/2016
• Issued: 04/23/2019
• Est. Priority Date: 10/07/2011
• Status: Active Grant

• Current Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Original Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Inventors: McNair, Douglas S., Murrish, John Christopher, Kailasam, Kanakasabha
• Primary Examiner(s): Le, Debbie M
• Application Number:
  US15/392,928
• Time in Patent Office: 846 Days
• Field of Search: None
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/3347 using vector based model
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

Systems, methods and computer-readable media are provided for facilitating patient health care by providing discovery, validation, and quality assurance of nomenclatural linkages between pairs of terms or combinations of terms in databases extant on multiple different health information systems that do not share a set of unified codesets, nomenclatures, or ontologies, or that may in part rely upon unstructured free-text narrative content instead of codes or standardized tags. Embodiments discover semantic structures existing naturally in documents and records, including relationships of synonymy and polysemy between terms arising from disparate processes, and maintained by different information systems. In some embodiments, this process is facilitated by applying Latent Semantic Analysis in concert with decision-tree induction and similarity metrics. In some embodiments, data is re-mined and regression testing is applied to new mappings against an existing mapping base, thereby permitting these embodiments to “learn” ontology mappings as clinical, operational, or financial patterns evolve.

Claims

1. One or more computer-readable storage devices having computer-executable instructions embodied thereon that when executed provide a method for facilitating decision support by determining nomenclature linkages between variables in databases that have different ontologies, the method comprising:

accessing a first document from a first record system having a first ontology;

accessing a second document from a second record system having a second ontology that is different than the first ontology;

determining categorical datatypes of the first document;

determining categorical datatypes of the second document;

based on the categorical datatypes of the first document and the categorical datatypes of the second document, generating a set of textmatrices;

applying latent semantic analysis to the set of textmatrices to determine a latent semantic space associated with at least one first-document variable and at least one second document variable;

specifying a threshold of similarity;

for a first comparison-variable from the at least one first-document variable associated with the latent semantic space;

determining a measure of similarity to a second-comparison variable from the at least one second-document variable associated with the latent semantic space;

performing a comparison of the measure of similarity to the threshold; and

based on the comparison, determining that the measure of similarity satisfies the threshold, associating the first comparison variable with the second comparison variable, and designating the association as a synonymy, wherein the threshold is satisfied if the measure of similarity is greater than the threshold.

2. The one or more computer-readable storage devices of claim 1, wherein the measure of similarity is determined using Salton'"'"'s cosine.

3. The one or more computer-readable storage devices of claim 2, wherein the threshold is specified as 0.62, and wherein the classifier is a decision-tree classifier.

4. The one or more computer-readable storage devices of claim 2, wherein the threshold is specified as 0.8 and further wherein the association is designated as a strong synonymy.

5. The one or more computer-readable storage devices of claim 1, wherein the measure of similarity is determined using Pearson'"'"'s correlation coefficient.

6. The one or more computer-readable storage devices of claim 1, wherein the first record system is a first electronic health record system for a first hospital, and the second record system is a second electronic health record system for a second hospital.

7. The one or more computer-readable storage devices of claim 1, wherein the first record system is designated as a “

gold standard.”

8. The one or more computer-readable storage devices of claim 1, wherein applying latent semantic analysis includes singular value decomposition.

9. The one or more computer-readable storage devices of claim 1, wherein the first document comprises a set of records from the first record system, and the second document comprises a set of records from the second record system.

10. The one or more computer-readable storage devices of claim 1, further comprising displaying to a user the first comparison variable and the second comparison variable as a designated synonymy.

11. The one or more computer-readable storage devices of claim 10, further comprising receiving an indication from the user confirming or rejecting the designated synonymy.

12. A system for facilitating decision support by determining nomenclature linkages between variables in databases having different ontologies, comprising:

one or more computer processors; and

one or more computer storage media storing computer-useable instructions that, when executed by the one or more processors, implement a method comprising;

accessing a first document from a first record system having a first ontology;

accessing a second document from a second record system having a second ontology that is different than the first ontology;

determining categorical datatypes of the first document;

determining categorical datatypes of the second document;

based on the categorical datatypes of the first document and the categorical datatypes of the second document, generating a set of textmatrices;

applying latent semantic analysis to the set of textmatrices to determine a latent semantic space associated with at least one first-document variable and at least one second document variable;

specifying a threshold of similarity;

for a first comparison-variable from the at least one first-document variable associated with the latent semantic space;

determining a measure of similarity to a second-comparison variable from at least one second-document variable associated with the latent semantic space;

performing a comparison of the measure of similarity to the threshold; and

based on the comparison, determining that the measure of similarity satisfies the threshold, associating the first comparison variable with the second comparison variable, and designating the association as a synonymy, wherein the threshold is satisfied if the measure of similarity is greater than the threshold.

13. The system of claim 12, wherein the measure of similarity is determined using Salton'"'"'s cosine.

14. The method of claim 13, wherein the threshold is specified as 0.62, and wherein the classifier is a decision-tree classifier.

15. The system of claim 12, wherein the measure of similarity is determined using Pearson'"'"'s correlation coefficient.

16. The system of claim 12, wherein the first record system is a first electronic health record system for a first hospital, and the second record system is a second electronic health record system for a second hospital.

17. The system of claim 12, wherein applying latent semantic analysis includes singular value decomposition.

18. The system of claim 12, wherein the first document comprises a set of records from the first record system, and the second document comprises a set of records from the second record system.

19. A system for discovering and validating latent relationships in data, comprising:

one or more processors; and

one or more computer storage media storing computer-useable instructions that, when executed by the one or more processors, implement a method comprising;

receiving a plurality of documents from two or more record-keeping systems, wherein the received plurality of documents comprises a set of documents;

determining categorical datatypes associated with each of the documents within the set of documents;

based on the set of documents, generating a set of textmatricies;

applying latent semantic analysis to the set of textmatrices to determine a latent semantic space;

specifying a threshold of similarity;

for a first-document variable, from a first document, associated with the latent semantic space;

determining a measure of similarity to a second-document variable, from a second document, associated with the latent semantic space;

performing a comparison of the measure of similarity to the threshold; and

based on the comparison, determining that the measure of similarity satisfies the threshold, associating the first-document variable with the second-document variable, and designating the association as a synonymy, wherein the threshold is satisfied if the measure of similarity is greater than the threshold.

20. The system of claim 19, wherein the measure of similarity is determined using Salton'"'"'s cosine.