Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment.

ABSTRACT: Sepsis is a potentially life-threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient's state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite decades of research, there's still debate among experts on optimal treatment. Here, we combine for the first time, distributional deep reinforcement learning with mechanistic physiological models to find personalized sepsis treatment strategies. Our method handles partial observability by leveraging known cardiovascular physiology, introducing a novel physiology-driven recurrent autoencoder, and quantifies the uncertainty of its own results. Moreover, we introduce a framework for uncertainty-aware de

SUBMITTER: Nanayakkara T 

PROVIDER: S-EPMC9931225 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature