Natural language processing to identify adverse drug events.
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.
Gysbers
Michael
M
BJC HealthCare, Center for Healthcare Quality & Effectiveness; Washington University School of Medicine, St. Louis, MO, USA.
Reichley
Richard
R
Kilbridge
Peter M
PM
Noirot
Laura
L
Nagarajan
Rakesh
R
Dunagan
W Claiborne
WC
Bailey
Thomas C
TC
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*