Optimizing drug therapy with Reinforcement Learning: The case of Anemia Management

Optimal management of anemia due to End-Stage Renal Disease (ESRD) is a challenging task to physicians due to large inter-subject variability in response to Erythropoiesis Stimulating Agents (ESA). We demonstrate that an optimal dosing strategy for ESA can be derived using Reinforcement Learning (RL) techniques. In this study, we show some preliminary results of using a batch RL method, called Fitted Q-Iteration, to derive optimal ESA dosing strategies from retrospective treatment data. Presented results show that such dosing strategies are superior to a standard ESA protocol employed by our dialysis facilities.