Semantic processing to identify adverse drug event information from black box warnings

AMIA Annu Symp Proc. 2014 Nov 14;2014:442-8. eCollection 2014.

Abstract

Adverse drug events account for two million combined injuries, hospitalizations, or deaths each year. Furthermore, there are few comprehensive, up-to-date, and free sources of drug information. Clinical decision support systems may significantly mitigate the number of adverse drug events. However, these systems depend on up-to-date, comprehensive, and codified data to serve as input. The DailyMed website, a resource managed by the FDA and NLM, contains all currently approved drugs. We used a semantic natural language processing approach that successfully extracted information for adverse drug events, at-risk conditions, and susceptible populations from black box warning labels on this site. The precision, recall, and F-score were, 94%, 52%, 0.67 for adverse drug events; 80%, 53%, and 0.64 for conditions; and 95%, 44%, 0.61 for populations. Overall performance was 90% precision, 51% recall, and 0.65 F-Score. Information extracted can be stored in a structured format and may support clinical decision support systems.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, N.I.H., Intramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Drug Labeling*
  • Drug-Related Side Effects and Adverse Reactions*
  • Feasibility Studies
  • Humans
  • Internet
  • Natural Language Processing*
  • Prescription Drugs / adverse effects*
  • Semantics
  • United States
  • United States Food and Drug Administration

Substances

  • Prescription Drugs