Predictions of adverse events during hospitalizations can be used in programs aimed at improving patient outcomes. A patient\u2019s risk for adverse events may be biased by temporal processes in\ufb02uenced by diagnostic and therapeutic activities, as well as by the overall evolution of the patient\u2019s pathophysiology over time. Representing and reasoning about temporal process promises to enhance the accuracy of inferences about risk. However, understanding temporal in\ufb02uences is challenging for a number of reasons, including the large number of variables, the large class imbalance, and the di\ufb03culty of de\ufb01ning ground truth for risk over time. We explore such challenges in the context of predicting an inpatient\u2019s daily risk of becoming colonized with Clostridium Di\ufb03cile. We present and evaluate di\ufb00erent methods for extracting risk processes from medical records. These results highlight the bene\ufb01t of including a temporal dimension when modeling patient risk.<\/p>\n","protected":false},"excerpt":{"rendered":"
Predictions of adverse events during hospitalizations can be used in programs aimed at improving patient outcomes. A patient\u2019s risk for adverse events may be biased by temporal processes in\ufb02uenced by diagnostic and therapeutic activities, as well as by the overall evolution of the patient\u2019s pathophysiology over time. 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