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.
|Number of pages
|AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
|Published - 2008
ASJC Scopus subject areas
- General Medicine