  Recent statistical and reinforcement learning methods have significantly
advanced patient care strategies. However, these approaches face substantial
challenges in high-stakes contexts, including missing data, inherent
stochasticity, and the critical requirements for interpretability and patient
safety. Our work operationalizes a safe and interpretable framework to identify
optimal treatment regimes. This approach involves matching patients with
similar medical and pharmacological characteristics, allowing us to construct
an optimal policy via interpolation. We perform a comprehensive simulation
study to demonstrate the framework's ability to identify optimal policies even
in complex settings. Ultimately, we operationalize our approach to study
regimes for treating seizures in critically ill patients. Our findings strongly
support personalized treatment strategies based on a patient's medical history
and pharmacological features. Notably, we identify that reducing medication
doses for patients with mild and brief seizure episodes while adopting
aggressive treatment for patients in intensive care unit experiencing intense
seizures leads to more favorable outcomes.
