Developing a section labeler for clinical documents

Peter J. Haug, Xinzi Wu, Jeffery P. Ferraro, Guergana K. Savova, Stanley M. Huff, Christopher G. Chute

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)636-644
Number of pages9
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2014
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)


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