Toward automatic recognition of high quality clinical evidence.

Halil Kilicoglu, Dina Demner-Fushman, Thomas C. Rindflesch, Nancy L. Wilczynski, R. Brian Haynes

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)368
Number of pages1
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2008
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)

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