Reinforcement learning approach to individualization of chronic pharmacotherapy

Effective pharmacological therapy in chronic treatments poses many challenges to physicians. Individual response to treatment varies across patient populations. Furthermore, due to the prolonged character of the therapy, the response may change over time. A reinforcement learning-based framework is proposed for treatment individualization in the management of renal anemia. The approach is based on numerical simulation of the patient performed by Takagi-Sugeno fuzzy model and a radial basis function network implementation of an on-policy Q-learning critic. Simulation results demonstrate the potential of the proposed method to yield policies that achieve the therapeutic goal in individuals with different response characteristics.