title: Developing a section labeler for clinical documents.
abstract: Natural language processing (NLP) technologies provide an opportunity to extract key patient data from free text documents within the electronic health record (EHR). We are developing a series of components from which to construct NLP pipelines. These pipelines typically begin with a component whose goal is to label sections within medical documents with codes indicating the anticipated semantics of their content. This Clinical Section Labeler prepares the document for further, focused information extraction. Below we describe the evaluation of six algorithms designed for use in a Clinical Section Labeler. These algorithms are trained with N-gram-based feature sets extracted from document sections and the document types. In the evaluation, 6 different Bayesian models were trained and used to assign one of 27 different topics to each section. A tree-augmented Bayesian network using the document type and N-grams derived from section headers proved most accurate in assigning individual sections appropriate section topics.
authors.affiliation: Intermountain Healthcare, Salt Lake City, UT ; University of Utah, Salt Lake City, UT.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Haug
authors.affiliation: Intermountain Healthcare, Salt Lake City, UT.
authors.forename: Xinzi
authors.initials: X
authors.lastname: Wu
authors.affiliation: Intermountain Healthcare, Salt Lake City, UT ; University of Utah, Salt Lake City, UT.
authors.forename: Jeffery P
authors.initials: JP
authors.lastname: Ferraro
authors.affiliation: Boston Children's Hospital and Harvard Medical School, Boston, MA.
authors.forename: Guergana K
authors.initials: GK
authors.lastname: Savova
authors.affiliation: Intermountain Healthcare, Salt Lake City, UT ; University of Utah, Salt Lake City, UT.
authors.forename: Stanley M
authors.initials: SM
authors.lastname: Huff
authors.affiliation: Mayo Clinic, Rochester, MN.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D057286:Electronic Health Records / Q000145:classification; D016247:Information Storage and Retrieval; D009323:Natural Language Processing; D012660:Semantics