title: Extracting drug-drug interaction articles from MEDLINE to improve the content of drug databases.
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.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
authors.forename: Stephany
authors.initials: S
authors.lastname: Duda
authors.forename: Constantin
authors.initials: C
authors.lastname: Aliferis
authors.forename: Randolph
authors.initials: R
authors.lastname: Miller
authors.forename: Alexander
authors.initials: A
authors.lastname: Statnikov
authors.forename: Kevin
authors.initials: K
authors.lastname: Johnson
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019992:Databases as Topic; D004347:Drug Interactions; D005240:Feasibility Studies; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D046650:Medical Subject Headings; D039781:PubMed; D012372:ROC Curve