TY - JOUR
T1 - Toward automatic recognition of high quality clinical evidence.
AU - Kilicoglu, Halil
AU - Demner-Fushman, Dina
AU - Rindflesch, Thomas C.
AU - Wilczynski, Nancy L.
AU - Haynes, R. Brian
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=73949095140&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=73949095140&partnerID=8YFLogxK
M3 - Article
C2 - 18998881
AN - SCOPUS:73949095140
SN - 1559-4076
SP - 368
JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
ER -