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

DeepSOFA: A Real-Time Continuous Acuity Score Framework using Deep Learning

Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require manual, time-consuming, and error-prone calculations that are further hindered by the use of static variable thresholds derived from aggregate patient populations. These coarse frameworks do not capture time-sensitive individual physiological patterns and are not suitable for instantaneous assessment of patients’ acuity trajectories, a critical task for the ICU where conditions often change rapidly. Furthermore, they are ill-suited to capitalize on the emerging availability of streaming electronic health recorddata. We propose a novel acuity score framework (DeepSOFA) that leverages temporal patient measurements in¬†conjunction with deep learning models to make accurate assessments of a patient’s illness severity at any point during their ICU stay. We compare DeepSOFA with SOFA baseline models using the same predictors and find that at any point during an¬† ICU admission, DeepSOFA yields more accurate predictions of in-hospital mortality. Read more on ArXiv!

graphs showing vital sign data and associated predicted mortality rate