title: Extraction of adverse drug effects from clinical records.
abstract: With the rapidly growing use of electronic health records, the possibility of large-scale clinical information extraction has drawn much attention. We aim to extract adverse drug events and effects from records. As the first step of this challenge, this study assessed (1) how much adverse-effect information is contained in records, and (2) automatic extracting accuracy of the current standard Natural Language Processing (NLP) system. Results revealed that 7.7% of records include adverse event information, and that 59% of them (4.5% in total) can be extracted automatically. This result is particularly encouraging, considering the massive amounts of records, which are increasing daily.
authors.affiliation: Center for Knowledge Structuring, University of Tokyo, University of Tokyo Hospital, Tokyo, Japan. eiji.aramaki@gmail.com
authors.forename: Eiji
authors.initials: E
authors.lastname: Aramaki
authors.forename: Yasuhide
authors.initials: Y
authors.lastname: Miura
authors.forename: Masatsugu
authors.initials: M
authors.lastname: Tonoike
authors.forename: Tomoko
authors.initials: T
authors.lastname: Ohkuma
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Masuichi
authors.forename: Kayo
authors.initials: K
authors.lastname: Waki
authors.forename: Kazuhiko
authors.initials: K
authors.lastname: Ohe
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D057225:Data Mining / Q000379:methods*; D003628:Database Management Systems; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D007564:Japan; D009323:Natural Language Processing; D018875:Vocabulary, Controlled