ArticleJournal of the American Medical Informatics Association : JAMIA
Using semantic and structural properties of the Unified Medical Language System to discover potential terminological relationships.
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. An erratum has been issued. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning.Biomedical informatics insights · 2016Article
- Multiple chronic conditions and disabilities: implications for health services research and data demands.Health services research · 2010Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
objectiveTo use the semantic and structural properties in the Unified Medical Language System (UMLS) Metathesaurus to characterize and discover potential relationships.
designThe UMLS integrates knowledge from several biomedical terminologies. This knowledge can be used to discover implicit semantic relationships between concepts. In this paper, the authors propose a problem-independent approach for discovering potential terminological relationships that employs semantic abstraction of indirect relationship paths to perform classification and analysis of network theoretical measures such as topological overlap, preferential attachment, graph partitioning, and number of indirect paths. Using different versions of the UMLS, the authors evaluate the proposed approach's ability to predict newly added relationships. MEASUREMENTS: Classification accuracy, precision-recall.
resultsStrong discriminative characteristics were observed with a semantic abstraction based classifier (classification accuracy of 91%), the average number of indirect paths, preferential attachment, and graph partitioning to identify potential relationships. The proposed relationship prediction algorithm resulted in 56% recall in top 10 results for new relationships added to subsequent versions of the UMLS between 2005 and 2007.
conclusionsThe UMLS has sufficient knowledge to enable discovery of potential terminological relationships.
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