A transformation-based method for auditing the IS-A hierarchy of biomedical terminologies in the Unified Medical Language System. (12th October 2020)

• Record Type: Journal Article
• Authors: Zheng, Fengbo; Shi, Jay; Yang, Yuntao; Zheng, W Jim; Cui, Licong
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 10(2020)
• Page Start: 1568
• Page End: 1575
• Publication Date: 2020-10-12
• Subjects: Unified Medical Language System -- biomedical terminologies -- SNOMED CT -- Gene Ontology -- quality assurance; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa123
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: The 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 Methods: Given 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. Results: Applying 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. Conclusions: Our results showed that our transformation-based method is effective in identifying missing IS-A relations in the UMLS source terminologies.