A Probabilistic Reasoning Method for Predicting the Progression of Clinical Findings from Electronic Medical Records

In this paper, we present a probabilistic reasoning method capable of generating predictions of the progression of clinical findings (CFs) reported in the narrative portion of electronic medical records. This method benefits from a probabilistic knowledge representation made possible by a graphical model. The knowledge encoded in the graphical model considers not only the CFs extracted from the clinical narratives, but also their chronological ordering (CO) made possible by a temporal inference technique described in this paper. Our experiments indicate that the predictions about the progression of CFs achieve high performance given the COs induced from patient records.