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Traditional methods for assessing illness severity and predicting in-hospitalmortality among critically ill patients require time-consuming, error-pronecalculations using static variable thresholds. These methods do not capitalizeon the emerging availability of streaming electronic health record data orcapture time-sensitive individual physiological patterns, a critical task inthe intensive care unit. We propose a novel acuity score framework (DeepSOFA)that leverages temporal measurements and interpretable deep learning models toassess illness severity at any point during an ICU stay. We compare DeepSOFAwith SOFA (Sequential Organ Failure Assessment) baseline models using the samemodel inputs and find that at any point during an ICU admission, DeepSOFAyields significantly more accurate predictions of in-hospital mortality. ADeepSOFA model developed in a public database and validated in a singleinstitutional cohort had a mean AUC for the entire ICU stay of 0.90 (95% CI0.90-0.91) compared with baseline SOFA models with mean AUC 0.79 (95% CI0.79-0.80) and 0.85 (95% CI 0.85-0.86). Deep models are well-suited to identifyICU patients in need of life-saving interventions prior to the occurrence of anunexpected adverse event and inform shared decision-making processes amongpatients, providers, and families regarding goals of care and optimal resourceutilization.

DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning