title: Identifying synonymy between SNOMED clinical terms of varying length using distributional analysis of electronic health records.
abstract: Medical terminologies and ontologies are important tools for natural language processing of health record narratives. To account for the variability of language use, synonyms need to be stored in a semantic resource as textual instantiations of a concept. Developing such resources manually is, however, prohibitively expensive and likely to result in low coverage. To facilitate and expedite the process of lexical resource development, distributional analysis of large corpora provides a powerful data-driven means of (semi-)automatically identifying semantic relations, including synonymy, between terms. In this paper, we demonstrate how distributional analysis of a large corpus of electronic health records - the MIMIC-II database - can be employed to extract synonyms of SNOMED CT preferred terms. A distinctive feature of our method is its ability to identify synonymous relations between terms of varying length.
authors.affiliation: Department of Computer and Systems Sciences (DSV), Stockholm University, Sweden.
authors.forename: Aron
authors.initials: A
authors.lastname: Henriksson
authors.affiliation: Division of Behavioral Medicine, Department of Family & Preventive Medicine, University of California, San Diego, USA.
authors.forename: Mike
authors.initials: M
authors.lastname: Conway
authors.affiliation: Department of Computer and Systems Sciences (DSV), Stockholm University, Sweden.
authors.forename: Martin
authors.initials: M
authors.lastname: Duneld
authors.affiliation: Division of Biomedical Informatics, Department of Medicine, University of California, San Diego, USA.
authors.forename: Wendy W
authors.initials: WW
authors.lastname: Chapman
mesh_terms: D057286:Electronic Health Records; D008962:Models, Theoretical; D009323:Natural Language Processing; D012660:Semantics; D013358:Subject Headings; D039061:Systematized Nomenclature of Medicine