Leveraging the semantic web and natural language processing to enhance drug-mechanism knowledge in drug product labels

Richard Boyce, Henk Harkema, Mike Conway

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Multiple studies indicate that drug-drug interactions are a significant source of preventable adverse drug events. Factors contributing to the occurrence of preventable ADEs resulting from DDIs include a lack of knowledge of the patient's concurrent medications and inaccurate or inadequate knowledge of interactions by health care providers. FDA-approved drug product labeling is a major source of information intended to help clinicians prescribe drugs in a safe and effective manner. Unfortunately, drug product labeling has been identified as often lagging behind emerging drug knowledge; especially when it has been several years since a drug has been released to the market. In this paper we report on a novel approach that explores employing Semantic Web technology and natural language processing to identify drug mechanism information that may update or expand upon statements present in product labeling.

Original languageEnglish
Title of host publicationIHI'10 - Proceedings of the 1st ACM International Health Informatics Symposium
Pages492-496
Number of pages5
DOIs
StatePublished - 2010
Externally publishedYes
Event1st ACM International Health Informatics Symposium, IHI'10 - Arlington, VA, United States
Duration: Nov 11 2010Nov 12 2010

Publication series

NameIHI'10 - Proceedings of the 1st ACM International Health Informatics Symposium

Conference

Conference1st ACM International Health Informatics Symposium, IHI'10
Country/TerritoryUnited States
CityArlington, VA
Period11/11/1011/12/10

Keywords

  • drug product labeling
  • drug-drug interactions
  • mash-up
  • semantic web

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