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

Authors:
Richard Boyce;Henk Harkema;Mike Conway
Affiliations:
University of Pittsburgh, Pittsburgh, PA, USA;University of Pittsburgh, Pittsburgh, PA, USA;University of Pittsburgh, Pittsburgh, PA, USA
Venue:
Proceedings of the 1st ACM International Health Informatics Symposium
Year:
2010

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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.