Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
Mathematical Methods in Medicine
Mathematical Methods in Medicine
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Improving management of Anemia in end stage renal disease using reinforcement learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Validation of a reinforcement learning policy for dosage optimization of erythropoietin
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
An integer programming approach for optimal drug dose computation
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
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 off-policy 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.