title: A pipeline to extract drug-adverse event pairs from multiple data sources.
abstract: BACKGROUND
  Pharmacovigilance aims to uncover and understand harmful side-effects of drugs, termed adverse events (AEs). Although the current process of pharmacovigilance is very systematic, the increasing amount of information available in specialized health-related websites as well as the exponential growth in medical literature presents a unique opportunity to supplement traditional adverse event gathering mechanisms with new-age ones.
  
  
  METHODS
  We present a semi-automated pipeline to extract associations between drugs and side effects from traditional structured adverse event databases, enhanced by potential drug-adverse event pairs mined from user-comments from health-related websites and MEDLINE abstracts. The pipeline was tested using a set of 12 drugs representative of two previous studies of adverse event extraction from health-related websites and MEDLINE abstracts.
  
  
  RESULTS
  Testing the pipeline shows that mining non-traditional sources helps substantiate the adverse event databases. The non-traditional sources not only contain the known AEs, but also suggest some unreported AEs for drugs which can then be analyzed further.
  
  
  CONCLUSIONS
  A semi-automated pipeline to extract the AE pairs from adverse event databases as well as potential AE pairs from non-traditional sources such as text from MEDLINE abstracts and user-comments from health-related websites is presented.
authors.forename: Srijyothsna
authors.initials: S
authors.lastname: Yeleswarapu
authors.affiliation: TCS Innovation Labs, Tata Consultancy Services Ltd, Deccan Park, 1, Software Units Layout, Madhapur, Hyderabad 500081, Andhra Pradesh, India. adityar.rao@tcs.com.
authors.forename: Aditya
authors.initials: A
authors.lastname: Rao
authors.forename: Thomas
authors.initials: T
authors.lastname: Joseph
authors.forename: Vangala Govindakrishnan
authors.initials: VG
authors.lastname: Saipradeep
authors.forename: Rajgopal
authors.initials: R
authors.lastname: Srinivasan
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards*; D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D009323:Natural Language Processing