Optimal clinical management of inherited chronic diseases, such as Cystic Fibrosis (CF), requires a dynamic approach which updates treatments to cope with the evolving course of illness and to tailor medicines and dosages for individual patient. In this paper, we examine the problem of computing optimal adaptive personalized therapy for CF patients. A temporal difference reinforcement learning method called fitted Q-iteration is utilized to discover the optimal treatment regimen directly from clinical data. We conduct a simulation study of virtual cystic fibrosis patients with Pseudomonas aeruginosa infection and antibiotic therapy with parameters tuned to approximately match published data from CF patients. Our simulation results indicate that reinforcement learning can be an effective tool in developing personalized therapy which optimises the benefit-risk trade off in multi-stage decision making and improves long term outcomes in chronic diseases.
Tang, Yiyun and Kosorok, Michael R., "Developing Adaptive Personalized Therapy for Cystic Fibrosis using Reinforcement Learning" (January 2012). The University of North Carolina at Chapel Hill Department of Biostatistics Technical Report Series. Working Paper 30.