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.
RL has previously been used in healthcare settings. Sepsis treatment strategy was tackled by @cite_0 , where models with discretized state and action-spaces were used to deduce treatment policies for septic patients. This work used value-iteration techniques to find an optimal policy . The optimal policy was evaluated by directly comparing the Q-values that would have been obtained while following it to those obtained when following the physician's policy. In this work, we build on this research by using continuous state-space models, deep reinforcement learning, and a clinically guided reward function. We extend our qualitative evaluation of the learned policies by examining how policies treat patients of different severity.
@cite_0
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Objective:To better define the incidence of sepsis and the characteristics of critically ill patients in European intensive care units.Design:Cohort, multiple-center, observational study.Setting:One hundred and ninety-eight intensive care units in 24 European countries.Patients:All new adult admissi