title: Recognizing Questions and Answers in EMR Templates Using Natural Language Processing.
abstract: Templated boilerplate structures pose challenges to natural language processing (NLP) tools used for information extraction (IE). Routine error analyses while performing an IE task using Veterans Affairs (VA) medical records identified templates as an important cause of false positives. The baseline NLP pipeline (V3NLP) was adapted to recognize negation, questions and answers (QA) in various template types by adding a negation and slot:value identification annotator. The system was trained using a corpus of 975 documents developed as a reference standard for extracting psychosocial concepts. Iterative processing using the baseline tool and baseline+negation+QA revealed loss of numbers of concepts with a modest increase in true positives in several concept categories. Similar improvement was noted when the adapted V3NLP was used to process a random sample of 318,000 notes. We demonstrate the feasibility of adapting an NLP pipeline to recognize templates.
authors.affiliation: VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
authors.forename: Guy
authors.initials: G
authors.lastname: Divita
authors.affiliation: VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
authors.forename: Shuying
authors.initials: S
authors.lastname: Shen
authors.affiliation: VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
authors.forename: Marjorie E
authors.initials: ME
authors.lastname: Carter
authors.affiliation: VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Redd
authors.affiliation: University of Utah School of Medicine, Salt Lake City, Utah, USA.
authors.forename: Tyler
authors.initials: T
authors.lastname: Forbush
authors.affiliation: VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
authors.forename: Miland
authors.initials: M
authors.lastname: Palmer
authors.affiliation: VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Samore
authors.affiliation: VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
authors.forename: Adi V
authors.initials: AV
authors.lastname: Gundlapalli
mesh_terms: D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000145:classification* / Q000458:organization & administration*; D005568:Forms and Records Control / Q000379:methods*; D000069550:Machine Learning; D009323:Natural Language Processing; D015203:Reproducibility of Results; D012660:Semantics; D012680:Sensitivity and Specificity; D018875:Vocabulary, Controlled