Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patients

Whilst just using terminal rewards does make sense directly from observational data. However, there as a clinical objective, such sparse reward choices induce is significant ambiguity on the control objective high sample complexity, and all RL applications to medicine and on the best reward choice for the standard RL are performed in an offline manner, using a fixed dataset of objective. In this work, we propose a clinically observed trajectories. In particular, for complex syndromes motivated control objective for critically ill patients, such as sepsis, given the enormous heterogeneity and complexities for which the value functions have a simple amongst patient trajectories, it is very unlikely that medical interpretation. Further, we present theoretical the extent and the variety of the currently available data results and adapt our method to a practical will cover the feasible range of physiologic states in any Deep RL algorithm, which can be used alongside case. Further, it is well known that even survivors face a any value based Deep RL method. We experiment significant readmission risk and a reduced life expectancy on a large sepsis cohort and show that our (Cuthbertson et al., 2013; Gritte et al., 2021). Therefore, not method produces results consistent with clinical all survivors are the same, and we may have to consider the knowledge.