Development of an automated assessment tool for MedWatch reports in the FDA adverse event reporting system.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Sep 1, 2017

Abstract

OBJECTIVE: As the US Food and Drug Administration (FDA) receives over a million adverse event reports associated with medication use every year, a system is needed to aid FDA safety evaluators in identifying reports most likely to demonstrate causal relationships to the suspect medications. We combined text mining with machine learning to construct and evaluate such a system to identify medication-related adverse event reports.

Authors

  • Lichy Han
    Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.
  • Robert Ball
    Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States.
  • Carol A Pamer
    Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.
  • Russ B Altman
    Departments of Medicine, Genetics and Bioengineering, Stanford University, Stanford, California, United States of America.
  • Scott Proestel
    Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.

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