Text categorization models for retrieval of high quality articles in internal medicine.
The discipline of Evidence Based Medicine (EBM) studies formal and quasi-formal methods for identifying high quality medical information and abstracting it in useful forms so that patients receive the best customized care possible [1]. Current computer-based methods for finding high quality information in PubMed and similar bibliographic resources utilize search tools that employ preconstructed Boolean queries. These clinical queries are derived from a combined application of (a) user interviews, (b) ad-hoc manual document quality review, and (c) search over a constrained space of disjunctive Boolean queries. The present research explores the use of powerful text categorization (machine learning) methods to identify content-specific and high-quality PubMed articles. Our results show that models built with the proposed approach outperform the Boolean based PubMed clinical query filters in discriminatory power.
Aphinyanaphongs
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Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
Aliferis
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D019540:Area Under Curve; D001185:Artificial Intelligence; D001499:Bayes Theorem; D019317:Evidence-Based Medicine; D016247:Information Storage and Retrieval / Q000379:methods*; D007388:Internal Medicine; D008962:Models, Theoretical; D039781:PubMed