ArticleJournal of biomedical informatics2018
Auditing SNOMED CT hierarchical relations based on lexical features of concepts in non-lattice subgraphs.
Article in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Real-World Use of Controlled Terminologies, Ontologies, and Vocabularies for Evidence Generation Across a Large International Observational Network: Challenges and Lessons Learned From a Mixed Method Study.JMIR medical informatics · 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 logical definitions and lexical features to detect missing IS-A relations in biomedical terminologies.Journal of biomedical semantics · 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
- Identifying Missing IS-A Relations in Orphanet Rare Disease Ontology.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Identification of missing hierarchical relations in the vaccine ontology using acquired term pairs.Journal of biomedical semantics · 2022Article
- An evidence-based lexical pattern approach for quality assurance of Gene Ontology relations.Briefings in bioinformatics · 2022Article
- Automated Identification of Missing IS-A Relations in the Human Phenotype Ontology.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- A Comparison of Exhaustive and Non-lattice-based Methods for Auditing Hierarchical Relations in Gene Ontology.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Outlier concepts auditing methodology for a large family of biomedical ontologies.BMC medical informatics and decision making · 2020Article
- Detecting missing IS-A relations in the NCI Thesaurus using an enhanced hybrid approach.BMC medical informatics and decision making · 2020Article
- Analysis of readability and structural accuracy in SNOMED CT.BMC medical informatics and decision making · 2020Article
- A transformation-based method for auditing the IS-A hierarchy of biomedical terminologies in the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- SSIF: Subsumption-based Sub-term Inference Framework to audit Gene Ontology.Bioinformatics (Oxford, England) · 2020Article
- Enhancing the Quality of Hierarchic Relations in the National Cancer Institute Thesaurus to Enable Faceted Query of Cancer Registry Data.JCO clinical cancer informatics · 2020Article
- Characterizing database granularity using SNOMED-CT hierarchy.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Observational
- A lexical-based approach for exhaustive detection of missing hierarchical IS-A relations in SNOMED CT.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
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4 authors.
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Abstract
objectiveWe introduce a structural-lexical approach for auditing SNOMED CT using a combination of non-lattice subgraphs of the underlying hierarchical relations and enriched lexical attributes of fully specified concept names. Our goal is to develop a scalable and effective approach that automatically identifies missing hierarchical IS-A relations.
methodsOur approach involves 3 stages. In stage 1, all non-lattice subgraphs of SNOMED CT's IS-A hierarchical relations are extracted. In stage 2, lexical attributes of fully-specified concept names in such non-lattice subgraphs are extracted. For each concept in a non-lattice subgraph, we enrich its set of attributes with attributes from its ancestor concepts within the non-lattice subgraph. In stage 3, subset inclusion relations between the lexical attribute sets of each pair of concepts in each non-lattice subgraph are compared to existing IS-A relations in SNOMED CT. For concept pairs within each non-lattice subgraph, if a subset relation is identified but an IS-A relation is not present in SNOMED CT IS-A transitive closure, then a missing IS-A relation is reported. The September 2017 release of SNOMED CT (US edition) was used in this investigation.
resultsA total of 14,380 non-lattice subgraphs were extracted, from which we suggested a total of 41,357 missing IS-A relations. For evaluation purposes, 200 non-lattice subgraphs were randomly selected from 996 smaller subgraphs (of size 4, 5, or 6) within the "Clinical Finding" and "Procedure" sub-hierarchies. Two domain experts confirmed 185 (among 223) suggested missing IS-A relations, a precision of 82.96%.
conclusionsOur results demonstrate that analyzing the lexical features of concepts in non-lattice subgraphs is an effective approach for auditing SNOMED CT.
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