TY - GEN
T1 - Optimizing drug therapy with reinforcement learning
T2 - 2011 International Joint Conference on Neural Network, IJCNN 2011
AU - Malof, Jordan M.
AU - Gaweda, Adam E.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80054742943&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2011.6033485
DO - 10.1109/IJCNN.2011.6033485
M3 - Conference contribution
AN - SCOPUS:80054742943
SN - 9781457710865
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2088
EP - 2092
BT - 2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
Y2 - 31 July 2011 through 5 August 2011
ER -