Natural language processing to identify adverse drug events.

Michael Gysbers, Richard Reichley, Peter M. Kilbridge, Laura Noirot, Rakesh Nagarajan, W. Claiborne Dunagan, Thomas C. Bailey

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish
Pages (from-to)961
Number of pages1
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2008


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