Knowledge-based methods to help clinicians find answers in MEDLINE.
OBJECTIVE
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.


METHODS
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.


METHODS
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.


CONCLUSIONS
The improvements in finding relevant MEDLINE citations due to knowledge-based processing show promise in assisting physicians to answer questions in clinical practice.
Sneiderman
Charles A
CA
National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. charlie@nlm.nih.gov
Demner-Fushman
Dina
D
Fiszman
Marcelo
M
Ide
Nicholas C
NC
Rindflesch
Thomas C
TC
D000043:Abstracting and Indexing; D016247:Information Storage and Retrieval / Q000379:methods*; D051188:Knowledge Bases; D016239:MEDLINE; D046650:Medical Subject Headings
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