ArticleJournal of the American Medical Informatics Association : JAMIA2020
A transformation-based method for auditing the IS-A hierarchy of biomedical terminologies in the Unified Medical Language System.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Quantitatively assessing the impact of the quality of SNOMED CT subtype hierarchy on cohort queries.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Development and application of Chinese medical ontology for diabetes mellitus.BMC medical informatics and decision making · 2024Article
- A substring replacement approach for identifying missing IS-A relations in SNOMED CT.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Terminology integration and inconsistency identification of adverse event terminology for Japanese medical devices using SPARQL.BMC medical informatics and decision making · 2022Article
- Knowledge Representation and Management: Interest in New Solutions for Ontology Curation.Yearbook of medical informatics · 2021Review
- A Comparison of Exhaustive and Non-lattice-based Methods for Auditing Hierarchical Relations in Gene Ontology.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- The UMLS knowledge sources at 30: indispensable to current research and applications in biomedical informatics.Journal of the American Medical Informatics Association : JAMIA · 2020Article
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5 authors.
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Abstract
objectiveThe Unified Medical Language System (UMLS) integrates various source terminologies to support interoperability between biomedical information systems. In this article, we introduce a novel transformation-based auditing method that leverages the UMLS knowledge to systematically identify missing hierarchical IS-A relations in the source terminologies. MATERIALS AND
methodsGiven a concept name in the UMLS, we first identify its base and secondary noun chunks. For each identified noun chunk, we generate replacement candidates that are more general than the noun chunk. Then, we replace the noun chunks with their replacement candidates to generate new potential concept names that may serve as supertypes of the original concept. If a newly generated name is an existing concept name in the same source terminology with the original concept, then a potentially missing IS-A relation between the original and the new concept is identified.
resultsApplying our transformation-based method to English-language concept names in the UMLS (2019AB release), a total of 39 359 potentially missing IS-A relations were detected in 13 source terminologies. Domain experts evaluated a random sample of 200 potentially missing IS-A relations identified in the SNOMED CT (U.S. edition) and 100 in Gene Ontology. A total of 173 of 200 and 63 of 100 potentially missing IS-A relations were confirmed by domain experts, indicating that our method achieved a precision of 86.5% and 63% for the SNOMED CT and Gene Ontology, respectively.
conclusionsOur results showed that our transformation-based method is effective in identifying missing IS-A relations in the UMLS source terminologies.
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