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Submitted on June 17, 2004
Accepted on November 17, 2004
Affiliation of the authors: 1 Department of Biomedical Informatics, Vanderbilt University, Nashville, TN; 2 Department of Mathematics, Vanderbilt University, Nashville, TN
* To whom correspondence should be addressed.
Objective Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponential growth of medical publications. The objective of this study was to apply machine learning techniques to automatically identify high quality, content-specific articles for one time period in internal medicine and compare their performance to the Boolean-based PubMed clinical query filters of Haynes, et. al.
Design The selection criteria of the ACP Journal Club for articles in internal medicine were the basis for identifying high quality articles in the areas of etiology, prognosis, diagnosis, and treatment. Naive Bayes, a specialized AdaBoost algorithm, and linear and polynomial support vector machines were applied to identify these articles.
Measurements The machine learning models were compared in each category to each other and to the clinical query filters using area under the receiver operating characteristic curves, 11-point average recall-precision, and a sensitivity/ specificity match method.
Results In most categories, the data-induced models have better or comparable sensitivity, specificity, and precision than the clinical query filters. The polynomial support vector machine models perform the best among all learning methods in ranking the articles as evaluated by area under the receiver operating curve and 11-point average recall-precision.
Conclusions This research shows that, using machine learning methods, it is possible to automatically build models for retrieving high quality, content-specific articles, using inclusion or citation by the ACP Journal Club as a gold standard, in a given time period in internal medicine that perform better than currently-used PubMed clinical query filters.
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