title: Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration's Adverse Event Reporting System Narratives.
abstract: BACKGROUND
  The Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS) is a repository of spontaneously-reported adverse drug events (ADEs) for FDA-approved prescription drugs. FAERS reports include both structured reports and unstructured narratives. The narratives often include essential information for evaluation of the severity, causality, and description of ADEs that are not present in the structured data. The timely identification of unknown toxicities of prescription drugs is an important, unsolved problem.
  
  
  OBJECTIVE
  The objective of this study was to develop an annotated corpus of FAERS narratives and biomedical named entity tagger to automatically identify ADE related information in the FAERS narratives.
  
  
  METHODS
  We developed an annotation guideline and annotate medication information and adverse event related entities on 122 FAERS narratives comprising approximately 23,000 word tokens. A named entity tagger using supervised machine learning approaches was built for detecting medication information and adverse event entities using various categories of features.
  
  
  RESULTS
  The annotated corpus had an agreement of over .9 Cohen's kappa for medication and adverse event entities. The best performing tagger achieves an overall performance of 0.73 F1 score for detection of medication, adverse event and other named entities.
  
  
  CONCLUSIONS
  In this study, we developed an annotated corpus of FAERS narratives and machine learning based models for automatically extracting medication and adverse event information from the FAERS narratives. Our study is an important step towards enriching the FAERS data for postmarketing pharmacovigilance.
authors.affiliation: University of Massachusetts Medical School, Worcester, MA, United States.
authors.forename: Balaji
authors.initials: B
authors.lastname: Polepalli Ramesh
authors.forename: Steven M
authors.initials: SM
authors.lastname: Belknap
authors.forename: Zuofeng
authors.initials: Z
authors.lastname: Li
authors.forename: Nadya
authors.initials: N
authors.lastname: Frid
authors.forename: Dennis P
authors.initials: DP
authors.lastname: West
authors.forename: Hong
authors.initials: H
authors.lastname: Yu
keywords: adverse drug events; natural language processing; pharmacovigilance