Data-Pooling Reinforcement Learning for Personalized Healthcare Intervention

We apply reinforcement learning to solve personalized post-discharge intervention problem. The ultimate goal is to reduce the 30-day hospital readmission rate under possible budget constraints. To deal with the issue of small sample size in each patient class for personalized intervention policy, we develop a new data-pooling estimator and the corresponding data-pooling RLSVI reinforcement learning algorithm. We establish theoretical performance guarantee for this data-pooling RLSVI algorithm and demonstrate its empirical success with a real hospital dataset.