  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.
