Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patients
This work addresses the challenge of defining effective treatment strategies for critically ill patients, but it is incremental as it adapts existing methods to a specific clinical context.
The paper tackled the problem of ambiguous control objectives in reinforcement learning for critically ill patients by proposing a clinically motivated objective with simple medical interpretation, and demonstrated its consistency with clinical knowledge on a large sepsis cohort.
There has been considerable interest in leveraging RL and stochastic control methods to learn optimal treatment strategies for critically ill patients, directly from observational data. However, there is significant ambiguity on the control objective and on the best reward choice for the standard RL objective. In this work, we propose a clinically motivated control objective for critically ill patients, for which the value functions have a simple medical interpretation. Further, we present theoretical results and adapt our method to a practical Deep RL algorithm, which can be used alongside any value based Deep RL method. We experiment on a large sepsis cohort and show that our method produces results consistent with clinical knowledge.