Optimizing drug therapy with reinforcement learning: The case of anemia management

Jordan M. Malof, Adam E. Gaweda

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
Pages2088-2092
Number of pages5
DOIs
StatePublished - 2011
Event2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA, United States
Duration: Jul 31 2011Aug 5 2011

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2011 International Joint Conference on Neural Network, IJCNN 2011
Country/TerritoryUnited States
CitySan Jose, CA
Period07/31/1108/5/11

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