ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
A lexical-based approach for exhaustive detection of missing hierarchical IS-A relations in SNOMED CT.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Structuring integration for patient-centered care: a review-informed ontology-driven modular front-end framework for digital health innovation.Frontiers in digital health · 2026Article
- Quantitatively assessing the impact of the quality of SNOMED CT subtype hierarchy on cohort queries.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Leveraging SNOMED CT for patient cohort identification over heterogeneous EHR data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Leveraging logical definitions and lexical features to detect missing IS-A relations in biomedical terminologies.Journal of biomedical semantics · 2024Article
- An Automated Approach for Identifying Erroneous IS-A Relations in SNOMED CT.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Identifying Missing IS-A Relations in SNOMED CT with Fine-Tuned Pre-trained Language Models and Non-lattice Subgraphs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Logical definition-based identification of potential missing concepts in SNOMED CT.BMC medical informatics and decision making · 2023Article
- A deep learning approach to identify missing is-a relations in SNOMED CT.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A substring replacement approach for identifying missing IS-A relations in SNOMED CT.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Incompleteness of ontologies affects the quality of downstream ontology-based applications. In this paper, we introduce a novel lexical-based approach to automatically detect potentially missing hierarchical IS-A relations in SNOMED CT. We model each concept with an enriched set of lexical features, by leveraging words and noun phrases in the name of the concept itself and the concept's ancestors. Then we perform subset inclusion checking to suggest potentially missing IS-A relations between concepts. We applied our approach to the September 2017 release of SNOMED CT (US edition) which suggested a total of 38,615 potentially missing IS-A relations. For evaluation, a domain expert reviewed a random sample of 100 missing IS-A relations selected from the "Clinical finding" sub-hierarchy, and confirmed 90 are valid (a precision of 90%). Additional review of invalid suggestions further revealed incorrect existing IS-A relations. Our results demonstrate that systematic analysis of the enriched lexical features of concepts is an effective approach to identify potentially missing hierarchical IS-A relations in SNOMED CT.
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33936515PMC8075518What OpenQuestion holds
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