Deep Reinforcement Learning for Sepsis Treatment

TL;DR

This paper introduces a deep reinforcement learning approach to develop interpretable treatment policies for sepsis, aiming to assist clinicians and improve patient survival rates.

Contribution

It presents a novel method using continuous state-space models and deep RL to derive treatment policies that align with clinical practices.

Findings

Learned policies are clinically interpretable

Policies resemble physician treatment strategies

Potential to aid decision-making and improve survival

Abstract

Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we propose an approach to deduce treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Our model learns clinically interpretable treatment policies, similar in important aspects to the treatment policies of physicians. The learned policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival.