Scenario-Oriented Information Extraction from Electronic Health Records

Providing a comprehensive set of relevant information at the point of care is crucial for making correct clinical decisions in a timely manner. Retrieval of scenario specific information from an extensive electronic health record (EHR) is a tedious, time consuming and error prone task. In this paper, we propose a model and a technique for extracting relevant clinical information with respect to the most probable diagnostic hypotheses in a clinical scenario. In the proposed technique, we first model the relationship between diseases, symptoms, signs and other clinical information as a graph and apply concept lattice analysis to extract all possible diagnostic hypotheses related to a specific scenario. Next, we identify relevant information regarding the extracted hypotheses and search for matching evidences in the patient's EHR. Finally, we rank the extracted information according to their relevancy to the hypotheses. We have assessed the usefulness of our approach in a clinical setting by modeling a challenging clinical problem as a case study.