1 / 14

Automatic Mapping of ICPC-2 PLUS Terms to the SNOMED CT Terminology

Automatic Mapping of ICPC-2 PLUS Terms to the SNOMED CT Terminology. Jon Patrick, Yefeng Wang, School of IT, University of Sydney Graeme Miller, Julie O’Halloran Family Medicine Research Centre, Uni of Sydney. Introduction. Mapping ICPC 2-PLUS to SNOMED CT

hao
Download Presentation

Automatic Mapping of ICPC-2 PLUS Terms to the SNOMED CT Terminology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Automatic Mapping of ICPC-2 PLUS Terms to the SNOMED CT Terminology Jon Patrick, Yefeng Wang, School of IT, University of Sydney Graeme Miller, Julie O’Halloran Family Medicine Research Centre, Uni of Sydney

  2. Introduction • Mapping ICPC 2-PLUS to SNOMED CT • Computerised Term Mapping Approach • Searching & Mapping  Approval & Validation

  3. Mapping Methodologies Semi-Automatic Mapping • Mapping using UMLS • Common CUI (Concept Unique Identifier) • Lexical Matching • String-Based Matching • Sense-Based Matching • Post-Coordination

  4. Mapping using UMLS 100+ different vocabularies, including ICPC2P (2000) and SNOMED CT (2002) • Common CUI mapping ICPC2P Term SCT Term UMLS CUI ICPC2P Term UMLS CUI SCT Term ICPC2P Term ICPC2P Vocabulary UMLS Metathesaurus SNOMED CT Vocabulary

  5. The UMLS Vocabularies • Concept Names and Sources C0000039|ENG|…|SNOMEDCT |FN|82991003|… C0000215|SPA|… |MTHSCTSPA |FN|86884000|… … C0000039|ENG|…|ICPC2P |FN|A01001|… … A01.001 mapped to SNOMED concept 82991003

  6. Matching Results 3448 ICPC 2-PLUS Terms mapped to 6557 SNOMED CT Concepts, 3326 (50.7%) mappings are best-fit mapping

  7. String-Based Matching • Normalized Term Matching • Remove attributes & punctuations • Remove stop words • Stemming, spelling variations • Ignore case, word order etc. • Expanded Term Matching • IUCD  Intra-Uterine Contraceptive Device • musculo  musculoskeletal • Substring Term Matching

  8. Synonym Matching • Utilizing thesaurus to explore the semantics. • Replace each word constituent with its semantic equivalent word • Synonyms • fever  febricity , pyrexia • Derivational related words • fever  feverish, feverous

  9. Post-coordination Matching • Break the pre-coordinated terms into atomic terms • Map each atomic term to SCT terms • Use top level categories to identify relationships • Qualification • Combination

  10. String-Based Matching Result

  11. Post-Coordination Result

  12. Mapping Evaluation • One to One matching, on the “best fit” • Done by FMRC experts • UMLS Mapping • String-Based Mapping • Not done for Post-coordination Mapping • 96.49% UMLS mapping candidates have at least one best-fit mapping. • 94.25% string-based mapping candidates have at least one best-fit mapping.

  13. Conclusion & Future Work Conclusion • Mapped 80% ICPC 2-PLUS terms to SNOMED CT. • UMLS & Lexical matching provide reliable mappings. • Post-coordination provides solution to content incompleteness issues. Future Work • Explore context semantics. • Use structural information and relationship in SNOMED CT. to refine mapping candidates. • Evaluate the post-coordination.

  14. Question

More Related