Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health record data or capture time-sensitive individual physiological patterns, a critical task in the intensive care unit. We propose a novel acuity score framework (DeepSOFA) that leverages temporal measurements and interpretable deep learning models to assess illness severity at any point during an ICU stay. Using the University of Florida Health Integrated Data Repository as Honest Broker, we created a single-center longitudinal dataset (referred to as UFHealth) that was extracted directly from the electronic medical records derived from 84,350 patients 18 years or older at University of Florida Health during their admissions between January 1, 2012 and April 1, 2016 as well as all encounters within one-year history and one-year follow-up.

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