document: Clinical and financial outcomes analysis with existing hospital patient records Existing patient records are a valuable resource for automated outcomes analysis and knowledge discovery . However , key clinical data in these records is typically recorded in unstructured form as free text and images , and most structured clinical information is poorly organized . Time-consuming interpretation and analysis is required to convert these records into structured clinical data . Thus , only a tiny fraction of this resource is utilized . We present REMIND , a Bayesian Framework for Reliable Extraction and Meaningful Inference from Nonstructured Data . REMIND integrates and blends the structured and unstructured clinical data in patient records to automatically created high-quality structured clinical data . This structuring allows existing patient records to be mined for quality assurance , regulatory compliance , and to relate financial and clinical factors . We demonstrate REMIND on two medical applications : ( a ) Extract `` recurrence '' , the key outcome for measuring treatment effectiveness , for colon cancer patients ( ii ) Extract key diagnoses and complications for acute myocardial infarction ( heart attack ) patients , and demonstrate the impact of these clinical factors on financial outcomes .
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