Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine.

ABSTRACT: Traditionally, precision medicine involves classifying patients to identify subpopulations that respond favorably to specific therapeutics. We pose precision medicine as a dynamic feedback control problem, where treatment administered to a patient is guided by measurements taken during the course of treatment. We consider sepsis, a life-threatening condition in which dysregulation of the immune system causes tissue damage. We leverage an existing simulation of the innate immune response to infection and apply deep reinforcement learning (DRL) to discover an adaptive personalized treatment policy that specifies effective multicytokine therapy to simulated sepsis patients based on systemic measurements. The learned policy achieves a dramatic reduction in mortality rate over a set of 500 simu

SUBMITTER: Petersen BK 

PROVIDER: S-EPMC6590719 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature