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

Author information

1
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.

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.

PMID:
16779033
PMCID:
PMC1560879
[Indexed for MEDLINE]
Free PMC Article

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