Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 42, Issue 5, Pages 801-813Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2008.10.002
Keywords
Natural language processing; Semantic processing; Automatic summarization; Evidence-based medicine; Knowledge representation; Artificial intelligence; Evaluation
Funding
- National Institutes of Health
- National Library of Medicine
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As the number of electronic biomedical textual resources increases, it becomes harder for physicians to find useful answers at the point of care. Information retrieval applications provide access to databases; however, little research has been done on using automatic summarization to help navigate the documents returned by these systems. After presenting a semantic abstraction automatic summarization system for MEDLINE citations, we concentrate on evaluating its ability to identify useful drug interventions for 53 diseases. The evaluation methodology uses existing sources of evidence-based medicine as surrogates for a physician-annotated reference standard. Mean average precision (MAP) and a clinical usefulness score developed for this study were computed as performance metrics. The automatic summarization system significantly outperformed the baseline in both metrics. The MAP gain was 0.17 (p < 0.01) and the increase in the overall score of clinical usefulness was 0.39 (p < 0.05). Published by Elsevier Inc.
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