Improving management of Anemia in end stage renal disease using reinforcement learning

Authors:
Adam E. Gaweda
Affiliations:
Department of Medicine, University of Louisville
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 4
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Abstract

We present a Reinforcement Learning approach to elicit individualized dose adjustment policies for patients suffering Anemia due to End Stage Renal Disease. Our goal is to achieve stable steady-state anemia management in patients with exhibiting different levels of treatment response. The approach uses Q-Iearning with parsimonious parametric representation of the state-action value function. We show that this approach achieves stability even in highly responsive patients.