Toward automatic recognition of high quality clinical evidence.
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
Halil
H
Concordia University, Department of Computer Science and Software Engineering, Montreal, Canada.
Demner-Fushman
Dina
D
Rindflesch
Thomas C
TC
Wilczynski
Nancy L
NL
Haynes
R Brian
RB
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