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Individualization of pharmacological anemia management using reinforcement learning

✍ Scribed by Adam E. Gaweda; Mehmet K. Muezzinoglu; George R. Aronoff; Alfred A. Jacobs; Jacek M. Zurada; Michael E. Brier

Publisher: Elsevier Science
Year: 2005
Tongue: English
Weight: 174 KB
Volume: 18
Category: Article
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2005.06.020

No coin nor oath required. For personal study only.

✦ Synopsis

Effective management of anemia due to renal failure poses many challenges to physicians. Individual response to treatment varies across patient populations and, due to the prolonged character of the therapy, changes over time. In this work, a Reinforcement Learning-based approach is proposed as an alternative method for individualization of drug administration in the treatment of renal anemia. Q-learning, an offpolicy approximate dynamic programming method, is applied to determine the proper dosing strategy in real time. Simulations compare the proposed methodology with the currently used dosing protocol. Presented results illustrate the ability of the proposed method to achieve the therapeutic goal for individuals with different response characteristics and its potential to become an alternative to currently used techniques.

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