ArticleJournal of the American Medical Informatics Association : JAMIA
Auditing the semantic completeness of SNOMED CT using formal concept analysis.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2021Article
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- A Lexical-based Formal Concept Analysis Method to Identify Missing Concepts in the NCI Thesaurus.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2020Article
- Complex overlapping concepts: An effective auditing methodology for families of similarly structured BioPortal ontologies.Journal of biomedical informatics · 2018Article
- An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies.Journal of biomedical informatics · 2018Article
- Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.Journal of biomedical informatics · 2018Review
- Identifying Similar Non-Lattice Subgraphs in Gene Ontology based on Structural Isomorphism and Semantic Similarity of Concept Labels.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Mining non-lattice subgraphs for detecting missing hierarchical relations and concepts in SNOMED CT.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Spark-MCA: Large-scale, Exhaustive Formal Concept Analysis for Evaluating the Semantic Completeness of SNOMED CT.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Article
- FEDRR: fast, exhaustive detection of redundant hierarchical relations for quality improvement of large biomedical ontologies.BioData mining · 2016Article
- Analyzing SNOMED CT's Historical Data: Pitfalls and Possibilities.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Scalable quality assurance for large SNOMED CT hierarchies using subject-based subtaxonomies.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- NEO: Systematic Non-Lattice Embedding of Ontologies for Comparing the Subsumption Relationship in SNOMED CT and in FMA Using MapReduce.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
- Quality Assurance of Cancer Study Common Data Elements Using A Post-Coordination Approach.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
- Using Semantic Web technology to support icd-11 textual definitions authoring.Journal of biomedical semantics · 2013Article
- Quality evaluation of value sets from cancer study common data elements using the UMLS semantic groups.Journal of the American Medical Informatics Association : JAMIA · 2012Article
- Auditing complex concepts of SNOMED using a refined hierarchical abstraction network.Journal of biomedical informatics · 2012Review
- Development and evaluation of an ontology for guiding appropriate antibiotic prescribing.Journal of biomedical informatics · 2012Article
- Large-scale, Exhaustive Lattice-based Structural Auditing of SNOMED CT.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2010Article
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2 authors.
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Abstract
objectiveThis study sought to develop and evaluate an approach for auditing the semantic completeness of the SNOMED CT contents using a formal concept analysis (FCA)-based model.
designWe developed a model for formalizing the normal forms of SNOMED CT expressions using FCA. Anonymous nodes, identified through the analyses, were retrieved from the model for evaluation. Two quasi-Poisson regression models were developed to test whether anonymous nodes can evaluate the semantic completeness of SNOMED CT contents (Model 1), and for testing whether such completeness differs between 2 clinical domains (Model 2). The data were randomly sampled from all the contexts that could be formed in the 2 largest domains: Procedure and Clinical Finding. Case studies (n = 4) were performed on randomly selected anonymous node samples for validation. MEASUREMENTS: In Model 1, the outcome variable is the number of fully defined concepts within a context, while the explanatory variables are the number of lattice nodes and the number of anonymous nodes. In Model 2, the outcome variable is the number of anonymous nodes and the explanatory variables are the number of lattice nodes and a binary category for domain (Procedure/Clinical Finding).
resultsA total of 5,450 contexts from the 2 domains were collected for analyses. Our findings revealed that the number of anonymous nodes had a significant negative correlation with the number of fully defined concepts within a context (p < 0.001). Further, the Clinical Finding domain had fewer anonymous nodes than the Procedure domain (p < 0.001). Case studies demonstrated that the anonymous nodes are an effective index for auditing SNOMED CT.
conclusionThe anonymous nodes retrieved from FCA-based analyses are a candidate proxy for the semantic completeness of the SNOMED CT contents. Our novel FCA-based approach can be useful for auditing the semantic completeness of SNOMED CT contents, or any large ontology, within or across domains.
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