  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. We compare DeepSOFA
with SOFA (Sequential Organ Failure Assessment) baseline models using the same
model inputs and find that at any point during an ICU admission, DeepSOFA
yields significantly more accurate predictions of in-hospital mortality. A
DeepSOFA model developed in a public database and validated in a single
institutional cohort had a mean AUC for the entire ICU stay of 0.90 (95% CI
0.90-0.91) compared with baseline SOFA models with mean AUC 0.79 (95% CI
0.79-0.80) and 0.85 (95% CI 0.85-0.86). Deep models are well-suited to identify
ICU patients in need of life-saving interventions prior to the occurrence of an
unexpected adverse event and inform shared decision-making processes among
patients, providers, and families regarding goals of care and optimal resource
utilization.

Machine Learning;Artificial Intelligence;Applications;Machine Learning