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Toward Automatic Recognition of High Quality Clinical Evidence.

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Kilicoglu H, Demner-Fushman D, Rindflesch TC, Wilczynski NL, Haynes RB
AMIA Annu Symp Proc. 2008 Nov 6:368
Abstract: 

Automatic methods for recognizing topically relevant documents supported by high quality research can assist clinicians in practicing evidence-based medicine. We approach the challenge of identifying articles with high quality clinical evidence as a binary classification problem. Combining predictions from supervised machine learning methods and using deep semantic features, we achieve 73.5% precision and 67% recall.

Kilicoglu H, Demner-Fushman D, Rindflesch TC, Wilczynski NL, Haynes RB. Toward Automatic Recognition of High Quality Clinical Evidence. AMIA Annu Symp Proc. 2008 Nov 6:368