Learning Evolving Patient Risk Processes for C. Diï¬" Colonization - Wiens, Jenna; Guttag, John; Horvitz, Eric (Appearing in the ICML 2012 Workshop on Clinical Data Analysis, Edinburgh, Scotland, UK, 2012)
Predictions of adverse events during hospitalizations can be used in programs aimed at improving patient outcomes. A patient’s risk for adverse events may be biased by temporal processes inﬂuenced by diagnostic and therapeutic activities, as well as by the overall evolution of the patient’s pathophysiology over time. Representing and reasoning about temporal process promises to enhance the accuracy of inferences about risk. However, understanding temporal inﬂuences is challenging for a number of reasons, including the large number of variables, the large class imbalance, and the diﬃculty of deﬁning ground truth for risk over time. We explore such challenges in the context of predicting an inpatient’s daily risk of becoming colonized with Clostridium Diﬃcile. We present and evaluate diﬀerent methods for extracting risk processes from medical records. These results highlight the beneﬁt of including a temporal dimension when modeling patient risk.