ArticleJournal of the American Medical Informatics Association : JAMIA2017
Mining non-lattice subgraphs for detecting missing hierarchical relations and concepts in SNOMED CT.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 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
- 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
- Concept Coverage Analysis of Ophthalmic Infections and Trauma among the Standardized Medical Terminologies SNOMED-CT, ICD-10-CM, and ICD-11.Ophthalmology science · 2023Article
- 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
- Leveraging non-lattice subgraphs for suggestion of new concepts for SNOMED CT.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
- 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
- A Lexical-based Formal Concept Analysis Method to Identify Missing Concepts in the NCI Thesaurus.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2020Article
- A review of auditing techniques for the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- 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
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6 authors.
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Abstract
objectiveQuality assurance of large ontological systems such as SNOMED CT is an indispensable part of the terminology management lifecycle. We introduce a hybrid structural-lexical method for scalable and systematic discovery of missing hierarchical relations and concepts in SNOMED CT. MATERIAL AND
methodsAll non-lattice subgraphs (the structural part) in SNOMED CT are exhaustively extracted using a scalable MapReduce algorithm. Four lexical patterns (the lexical part) are identified among the extracted non-lattice subgraphs. Non-lattice subgraphs exhibiting such lexical patterns are often indicative of missing hierarchical relations or concepts. Each lexical pattern is associated with a potential specific type of error.
resultsApplying the structural-lexical method to SNOMED CT (September 2015 US edition), we found 6801 non-lattice subgraphs that matched these lexical patterns, of which 2046 were amenable to visual inspection. We evaluated a random sample of 100 small subgraphs, of which 59 were reviewed in detail by domain experts. All the subgraphs reviewed contained errors confirmed by the experts. The most frequent type of error was missing is-a relations due to incomplete or inconsistent modeling of the concepts.
conclusionsOur hybrid structural-lexical method is innovative and proved effective not only in detecting errors in SNOMED CT, but also in suggesting remediation for these errors.
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