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

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 journal › Article › peer-review

10 Scopus citations

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.

Original language	English
Pages (from-to)	961
Number of pages	1
Journal	AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
State	Published - 2008

Cite this

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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.",

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AU - Gysbers, Michael

AU - Reichley, Richard

AU - Kilbridge, Peter M.

AU - Noirot, Laura

AU - Nagarajan, Rakesh

AU - Dunagan, W. Claiborne

AU - Bailey, Thomas C.

PY - 2008

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N2 - 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.

AB - 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.

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M3 - Article

C2 - 18999130

AN - SCOPUS:73949088594

SN - 1559-4076

SP - 961

JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

ER -

Natural language processing to identify adverse drug events.

Abstract

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