Sepsis is a leading cause of mortality in intensive care units (ICUs) 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. Understanding more about a patient's physiological state at a given time could hold the key to effective treatment policies. In this work, we propose a new approach to deduce optimal treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Learning treatment policies over continuous spaces is important, because we retain more of the patient's physiological information. Our model is able to learn clinically interpretable treatment policies, similar in important aspects to the treatment policies of physicians. Evaluating our algorithm on past ICU patient data, we find that our model could reduce patient mortality in the hospital by up to 3.6 over observed clinical policies, from a baseline mortality of 13.7 . The learned treatment policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival.
Optimal sepsis treatment strategy was tackled most recently by @cite_3 , using a discretized state and action-space to deduce optimal treatment policies for septic patients. Their work applied on-policy SARSA learning to fit an action-value function to the physician policy and value-iteration techniques to find an optimal policy . The optimal policy was then evaluated by comparing the Q-values that would have been obtained following chosen actions to the Q-values obtained by the physicians. We reproduce a similar model as our baseline, using related data pre-processing and clustering techniques. We additionally build on this approach by extending the results to the continuous domain, where policies are learned directly from the physiological state data, without discretization. We also propose a novel evaluation metric, different from ones used in @cite_3 . We focus on in-hospital mortality instead of 90-day mortality (used in @cite_3 ) because of the other unobserved factors that could affect mortality in a 3-month timeframe.
@cite_3
2580396496
Purpose The optimal strategy of fluid resuscitation in the early hours of severe sepsis and septic shock is controversial, with both an aggressive and conservative approach being recommended.