This Article Full Text Full Text (PDF) All Versions of this Article: M2407v1 14/6/772    most recent Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Sneiderman, C. A. Articles by Rindflesch, T. C. Search for Related Content PubMed PubMed Citation Articles by Sneiderman, C. A. Articles by Rindflesch, T. C.

Knowledge-based Methods to Help Clinicians Find Answers in MEDLINE

^a Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD
^b Graduate School of Medicine, University of Tennessee, Knoxville, TN.

^_* Correspondence: Charles Sneiderman, MD, PhD, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894 (Email: charlie{at}nlm.nih.gov).

Received for publication: 02/21/07; accepted for publication: 07/26/07.

Objectives: Large databases of published medical research can support clinical decision making by providing physicians with the best available evidence. The time required to obtain optimal results from these databases using traditional systems often makes accessing the databases impractical for clinicians. This article explores whether a hybrid approach of augmenting traditional information retrieval with knowledge-based methods facilitates finding practical clinical advice in the research literature.

Design: Three experimental systems were evaluated for their ability to find MEDLINE citations providing answers to clinical questions of different complexity. The systems (SemRep, Essie, and CQA-1.0), which rely on domain knowledge and semantic processing to varying extents, were evaluated separately and in combination. Fifteen therapy and prevention questions in three categories (general, intermediate, and specific questions) were searched. The first 10 citations retrieved by each system were randomized, anonymized, and evaluated on a three-point scale. The reasons for ratings were documented.

Measurements: Metrics evaluating the overall performance of a system (mean average precision, binary preference) and metrics evaluating the number of relevant documents in the first several presented to a physician were used.

Results: Scores (mean average precision = 0.57, binary preference = 0.71) for fusion of the retrieval results of the three systems are significantly (p < 0.01) better than those for any individual system. All three systems present three to four relevant citations in the first five for any question type.

Conclusion: The improvements in finding relevant MEDLINE citations due to knowledge-based processing show promise in assisting physicians to answer questions in clinical practice.