title: Toward automatic recognition of high quality clinical evidence.
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.
authors.affiliation: Concordia University, Department of Computer Science and Software Engineering, Montreal, Canada.
authors.forename: Halil
authors.initials: H
authors.lastname: Kilicoglu
authors.forename: Dina
authors.initials: D
authors.lastname: Demner-Fushman
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Rindflesch
authors.forename: Nancy L
authors.initials: NL
authors.lastname: Wilczynski
authors.forename: R Brian
authors.initials: RB
authors.lastname: Haynes
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002170:Canada; D019317:Evidence-Based Medicine; D016239:MEDLINE; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic; D011786:Quality Control; D012660:Semantics; D018875:Vocabulary, Controlled