ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Spark-MCA: Large-scale, Exhaustive Formal Concept Analysis for Evaluating the Semantic Completeness of SNOMED CT.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Extending import detection algorithms for concept import from two to three biomedical terminologies.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
- Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.Journal of biomedical informatics · 2018Review
- Matching biomedical ontologies based on formal concept analysis.Journal of biomedical semantics · 2018Article
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3 authors.
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Abstract
The completeness of a medical terminology system consists of two parts: complete content coverage and complete semantics. In this paper, we focus on semantic completeness and present a scalable approach, called Spark-MCA, for evaluating the semantic completeness of SNOMED CT. We formulate the SNOMED CT contents into an FCA-based formal context, in which SNOMED CT concepts are used for extents, while their attributes are used as intents. We applied Spark-MCA to the 201403 US edition of SNOMED CT to exhaustively compute all the formal concepts and sub concept relationships in about 2 hours with 96 processors using an Amazon Web Service cluster. We found a total of 799,868 formal concepts, within which 500,583 are not contained in the 201403 release. We compared these concepts with the cumulative addition of 22,687 concepts from the 5 "delta" files from the 201403 release to the 201609 release. 3,231 matches were found between those suggested by FCA and those from cumulative concept addition by the SNOMED CT Editorial Panel. This result provides encouraging evidence that our approach could be useful for enhancing the semantic completeness of SNOMED CT.
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29854265PMC5977568What OpenQuestion holds
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