Adaptive treatment of anemia on hemodialysis patients: A reinforcement learning approach

The aim of this work is to study the applicability of reinforcement learning methods to design adaptive treatment strategies that optimize, in the long-term, the dosage of erythropoiesis-stimulating agents (ESAs) in the management of anemia in patients undergoing hemodialysis. Adaptive treatment strategies are recently emerging as a new paradigm for the treatment and long-term management of the chronic disease. Reinforcement Learning (RL) can be useful to extract such strategies from clinical data, taking into account delayed effects and without requiring any mathematical model. In this work, we focus on the so-called Fitted Q Iteration algorithm, a RL approach that deals with the data very efficiently. Achieved results show the suitability of the proposed RL policies that can improve the performance of the treatment followed in the clinics. The methodology can be easily extended to other problems of drug dosage optimization.