Deep Reinforcement Learning for Sepsis Treatment
This addresses sepsis treatment for intensive care clinicians, but it is incremental as it builds on existing deep reinforcement learning methods.
The paper tackled the problem of sepsis treatment by using deep reinforcement learning to deduce clinically interpretable policies, which could aid clinicians and improve patient survival likelihood.
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.