title: Natural language processing to identify adverse drug events.
abstract: We tested and adapted Cancer Text Information Extraction System (caTIES), a publicly available natural language processing tool (NLP), as a method for identifying terms suggestive of adverse drug events (ADEs). Although caTIES was intended to extract concepts from surgical pathology reports, we report that it can successfully be used to search for ADEs on a much broader range of documents.
authors.affiliation: BJC HealthCare, Center for Healthcare Quality & Effectiveness; Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Gysbers
authors.forename: Richard
authors.initials: R
authors.lastname: Reichley
authors.forename: Peter M
authors.initials: PM
authors.lastname: Kilbridge
authors.forename: Laura
authors.initials: L
authors.lastname: Noirot
authors.forename: Rakesh
authors.initials: R
authors.lastname: Nagarajan
authors.forename: W Claiborne
authors.initials: WC
authors.lastname: Dunagan
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Bailey
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000458:organization & administration*; D000465:Algorithms; D001185:Artificial Intelligence; D064420:Drug-Related Side Effects and Adverse Reactions / Q000145:classification* / Q000175:diagnosis*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D016347:Medical Records Systems, Computerized / Q000458:organization & administration*; D008923:Missouri; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*