Extracting drug-drug interaction articles from MEDLINE to improve the content of drug databases

AMIA Annu Symp Proc. 2005;2005:216-20.

Abstract

Drug-drug interaction systems exhibit low signal-to-noise ratios because of the amount of clinically insignificant or inaccurate information they contain. MEDLINE represents a respected source of peer-reviewed biomedical citations that potentially might serve as a valuable source of drug-drug interaction information, if relevant articles could be pinpointed effectively and efficiently. We evaluated the classification capability of Support Vector Machines as a method for locating articles about drug interactions. We used a corpus of "positive" and"negative" drug interaction citations to generate datasets composed of MeSH terms, CUI-tagged title and abstract text, and stemmed text words. The study showed that automated classification techniques have the potential to perform at least as well as PubMed in identifying drug-drug interaction articles.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Artificial Intelligence*
  • Databases as Topic
  • Drug Interactions*
  • Feasibility Studies
  • Humans
  • Information Storage and Retrieval / methods*
  • MEDLINE*
  • Medical Subject Headings
  • PubMed
  • ROC Curve