Analyzing the Semantics of patient data to rank records of literature retrieval

Analyzing the Semantics of Patient Data to Rank Records of Literature Retrieval Eneida A. Mendonça Department of Medical Informatics Columbia University em264@columbia.edu Stephen B. Johnson Department of Medical Informatics Columbia University sbj2@columbia.edu Yoon-Ho Seol Department of Medical Informatics Columbia University seol@dmi.columbia.edu James J. Cimino Department of Medical Informatics Columbia University jjc7@columbia.edu Abstract We describe the use of clinical data present in the medical record to determine the relevance of research evidence from literature databases. We studied the effect of using automated knowledge approaches as compared to physician’s selection of articles, when using a traditional information retrieval system. Three methods were evaluated. The first method identified terms and their semantics and relationships in the patient’s record to build a map of the record, which was represented in conceptual graph notation. This approach was applied to data in an individual’s medical record and used to score citations retrieved using a graph matching algorithm. The second method identified associations between terms in the medical record, assigning them semantic types and weights based on the co-occurrence of these associations in citations of biomedical literature. The method was applied to data in an individual’s medical record and used to score citations. The last method combined the first two. The results showed that physicians agreed better with each other than with the automated methods. However, we found a significant positive relation between physicians’ selection of abstracts and two of the methods. We believe the results encourage the use of clinical data to determine the relevance of medical literature to the care of individual patients. 1 Introduction The practice of evidence-based medicine, which gained popularity in the last decade, has encouraged clinicians to understand and utilize critically appraised published research evidence. The tremendous increase of biomedical knowledge resources in electronic form, particularly on the World Wide Web, has generated a great deal of interest. The increased availability of information does not make it easy for clinicians to filter large amounts of information and incorporate evidence to clinical practice. Although the number of clinicians and medical students who routinely perform their own searches has increased, they still have difficulty keeping-up-to-date with advances in medical science. (Gorman and Helfand, 1995) Decision support tools designed to provide relevant and current evidence to clinicians promise to substantially improve health care quality (Haynes, Hayward, and Lomas, 1995 ;Rodrigues, 2000 ;Sim, et al., 2001) and potentially reduce medical errors.(Bates, et al., 2001) Such tools include those that facilitate the access to, extraction of, and summarization of evidence. The Evidence and Decision Support track of the 2000 AMIA Spring Symposium examined the challenges in the development and adoption of clinical decision support systems for evidence-based practice.(Sim, et al., 2001) The speakers for the Evidence and Decision Support track described five central areas of activity as essential for the adoption of those systems. Two of the areas were a) the capture of both literature-based and practice based research evidence into machine-interpretable form, and b) the establishment of a technical and methodological foundation for applying research …

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