Analyzing the Semantics of Patient Data to Rank Records of Literature Retrieval

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.