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An extraordinary amount of clinical information is available within Electronic Medical Records. However, interpreting this knowledge typically demands a significant level of clinical understanding. This can facilitated by access to structured knowledge bases. However, even if vast, biomedical knowledge bases have very limited relational information available. In contrast, clinical text expresses many relations between concepts using an extraordinary amount of variation regarding the author's belief state - whether a medical concept is present, uncertain, or absent. In this paper, we propose a method for automatically constructing a graph of clinically related concepts based on their belief state. For this purpose, we first devise a method for classifying the belief state of certain medical concepts. Second, we designed a technique for constructing a graph of related medical concepts qualified by the physician's belief value. Thirdly, we demonstrate several techniques for inferring the similarity between qualified medical concepts, and present a generalized algorithm for determining the second-order similarity between qualified medical concepts. Finally, we show that incorporating the knowledge encoded from this graph yield competitive results when applied to query expansion for the retrieval of hospital patient cohorts.
Goodwin et al. (Sun,) studied this question.