title: Document Sublanguage Clustering to Detect Medical Specialty in Cross-institutional Clinical Texts.
abstract: This paper reports on a set of studies designed to identify sublanguages in documents for domain-specific processing across institutions. Psychological evidence indicates that humans use context-specific linguistic information when they read. Natural Language Processing (NLP) pipelines are successful within specific domains (i.e., contexts). To limit the number of domain-specific NLP systems, a natural focus would be on sublanguages. Sublanguages are identified by shared lexical and semantic features.[1] Patterson and Hurdle[2] developed a sublanguage identification system that functioned well for 12 clinical specialties at the University of Utah. The current work compares sublanguages across institutions. Using a clinical NLP pipeline augmented by a new document corpus from the University of Pittsburg (UPitt), new documents were assigned to clusters based on the minimum cosine-distance to a Utah cluster centroid. The UPitt documents were divided into a nine-group specialty corpus. Across institutions, five of the specialty groups fell within the expected clusters. We find that clustering encounters difficulty due to documents with mixed sublanguages; naming convention differences across institutions; and document types used across specialties. The findings indicate that clinical specialty sublanguages can be identified across institutions.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Health Sciences Center, Salt Lake City, UT.
authors.forename: Kristina
authors.initials: K
authors.lastname: Doing-Harris
authors.affiliation: VA SLC Health Care, Salt Lake City, UT.
authors.forename: Olga
authors.initials: O
authors.lastname: Patterson
authors.affiliation: Department of Biomedical Informatics, University of Utah, Health Sciences Center, Salt Lake City, UT.
authors.forename: Sean
authors.initials: S
authors.lastname: Igo
authors.affiliation: Department of Biomedical Informatics, University of Utah, Health Sciences Center, Salt lake City, UT.
authors.forename: John
authors.initials: J
authors.lastname: Hurdle
keywords: Medical informatics applications; cognitive science; natural language processing