Multi-group Reinforcement Learning for Electrolyte Repletion

Most off-policy reinforcement learning methods for specifying treatment policies in EHR data have a heterogenous patient population as well as different complications that are generally not considered in identifying optimal treatment policies as patient subgroups are hard to model. In this work, we use multi-group Gaussian process regression in a fitted Q-iteration framework to model diverse patient subgroups and adapt the optimal policies in a personalized manner as we approximate these functions across the full patient population. We apply our multi-group reinforcement learning (MGRL) model in specifying optimal treatment policies in recommending electrolyte repletion to ICU patients with several comorbidities in different ethnic groups. When utilized in clinical settings, we show that these policies learn interpretable differences in the datasets for the distinct patient subgroups.