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UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesaurus ...
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Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus ...
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Assessing the enrichment of dietary supplement coverage in the Unified Medical Language System
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In: J Am Med Inform Assoc (2020)
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IMIA LaMB WG publications: Medical Concept Representation ‐ the Years Beyond 2000 ...
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IMIA LaMB WG publications: Medical Concept Representation ‐ the Years Beyond 2000 ...
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IMIA LaMB WG publications: 'Biomedical Semantics in the Big Data Era' - conference paper introducing the related workshop, MEDINFO, 2015 ...
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IMIA LaMB WG publications: 'Biomedical Semantics in the Big Data Era' - conference paper introducing the related workshop, MEDINFO, 2015 ...
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Auditing SNOMED CT Hierarchical Relations Based on Lexical Features of Concepts in Non-Lattice Subgraphs
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In: Computer Science Faculty Publications (2018)
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Interoperability of Medication Classification Systems: Lessons Learned Mapping Established Pharmacologic Classes (EPCs) to SNOMED CT
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Auditing SNOMED CT hierarchical relations based on lexical features of concepts in non-lattice subgraphs
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“Hybrid Topics” -- Facilitating the Interpretation of Topics Through the Addition of MeSH Descriptors to Bags of Words
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Mining Non-Lattice Subgraphs for Detecting Missing Hierarchical Relations and Concepts in SNOMED CT
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In: Computer Science Faculty Publications (2017)
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Abstract:
Objective: Quality 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 Methods: All 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. Results: Applying 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. Conclusions: Our 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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Keyword:
Computer Sciences; Data Mining; Health Care; Health Information Technology; non-lattice subgraph; ontology; quality assurance; SNOMED CT; Subject Headings; Systematized Nomenclature of Medicine
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URL: https://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1030&context=cs_facpub https://uknowledge.uky.edu/cs_facpub/31
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Context-Driven Automatic Subgraph Creation for Literature-Based Discovery
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In: Kno.e.sis Publications (2015)
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iExplore: Interactive Browsing and Exploring Biomedical Knowledge
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In: Amit P. Sheth (2014)
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A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
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In: Krishnaprasad Thirunarayan (2014)
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Context-Driven Automatic Subgraph Creation for Literature-Based Discovery
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In: Amit P. Sheth (2014)
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Context-Driven Automatic Subgraph Creation for Literature-Based Discovery
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In: Krishnaprasad Thirunarayan (2014)
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A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
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In: Amit P. Sheth (2014)
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iExplore: Interactive Browsing and Exploring Biomedical Knowledge
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In: Krishnaprasad Thirunarayan (2014)
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A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
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In: Kno.e.sis Publications (2013)
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