title: Document clustering of clinical narratives: a systematic study of clinical sublanguages.
abstract: It is widely believed that different clinical domains use their own sublanguage in clinical notes, complicating natural language processing, but this has never been demonstrated on a broad selection of note types. Starting from formal sublanguage theory, we constructed a feature space based on vocabulary and semantic types used in 17 different clinical domains by three author types (physicians, nurses, and social workers) in both the in- and outpatient settings. We supplied the resulting vectors to CLUTO, a robust clustering tool suitable for this high-dimensional space. Our results confirm that note types with a broad clinical scope, e.g, History & Physicals and Discharge Summaries, cluster together, while note types with a narrow clinical scope form surprisingly pure, disjoint sublanguages. A reasonable conclusion from this study is that any tool relying on term statistics or semantics trained on one clinical note type may not work well on any other.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
authors.forename: Olga
authors.initials: O
authors.lastname: Patterson
authors.forename: John F
authors.initials: JF
authors.lastname: Hurdle
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D006784:Hospitals, Teaching; D006801:Humans; D008499:Medical Records; D033262:Narration; D009323:Natural Language Processing; D009726:Nurses; D010820:Physicians; D012660:Semantics; D012947:Social Work; D014825:Vocabulary