Answering Clinical Questions with Knowledge-Based and Statistical Techniques

cally identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidencebased medicine. Starting with an initial list of citations retrieved by PubMed, our system g relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians’ questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline.

• Type: Journal
• Year: 2007
• Where: COLING
• Authors: Dina Demner-Fushman, Jimmy J. Lin
• Keywords: Abstracts, COLING 2007, COLING 2008, Extracted Elements, Relevant Aspects