Journal Article

Active Computerized Pharmacovigilance Using Natural Language Processing, Statistics, and Electronic Health Records: A Feasibility Study

Xiaoyan Wang, George Hripcsak, Marianthi Markatou and Carol Friedman

in Journal of the American Medical Informatics Association

Published on behalf of American Medical Informatics Association

Volume 16, issue 3, pages 328-337
Published in print May 2009 | ISSN: 1067-5027
Published online May 2009 | e-ISSN: 1527-974X | DOI: https://dx.doi.org/10.1197/jamia.M3028
Active Computerized Pharmacovigilance Using Natural Language Processing, Statistics, and Electronic Health Records: A Feasibility Study

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  • Medical Statistics and Methodology
  • Bioinformatics and Computational Biology
  • Biomathematics and Statistics

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Abstract

Objective: It is vital to detect the full safety profile of a drug throughout its market life. Current pharmacovigilance systems still have substantial limitations, however. The objective of our work is to demonstrate the feasibility of using natural language processing (NLP), the comprehensive Electronic Health Record (EHR), and association statistics for pharmacovigilance purposes.

Design: Narrative discharge summaries were collected from the Clinical Information System at New York Presbyterian Hospital (NYPH). MedLEE, an NLP system, was applied to the collection to identify medication events and entities which could be potential adverse drug events (ADEs). Co-occurrence statistics with adjusted volume tests were used to detect associations between the two types of entities, to calculate the strengths of the associations, and to determine their cutoff thresholds. Seven drugs/drug classes (ibuprofen, morphine, warfarin, bupropion, paroxetine, rosiglitazone, ACE inhibitors) with known ADEs were selected to evaluate the system.

Results: One hundred thirty-two potential ADEs were found to be associated with the 7 drugs. Overall recall and precision were 0.75 and 0.31 for known ADEs respectively. Importantly, qualitative evaluation using historic roll back design suggested that novel ADEs could be detected using our system.

Conclusions: This study provides a framework for the development of active, high-throughput and prospective systems which could potentially unveil drug safety profiles throughout their entire market life. Our results demonstrate that the framework is feasible although there are some challenging issues. To the best of our knowledge, this is the first study using comprehensive unstructured data from the EHR for pharmacovigilance.

Journal Article.  7503 words.  Illustrated.

Subjects: Medical Statistics and Methodology ; Bioinformatics and Computational Biology ; Biomathematics and Statistics

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