Extracting drug-drug interaction articles from MEDLINE to improve the content of drug databases.
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.
Duda
Stephany
S
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
Aliferis
Constantin
C
Miller
Randolph
R
Statnikov
Alexander
A
Johnson
Kevin
K
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