Safe and Interpretable Estimation of Optimal Treatment Regimes
This work addresses the need for interpretable and safe treatment optimization in healthcare, particularly for critically ill patients, though it appears incremental by building on existing matching and interpolation methods.
The paper tackled the problem of identifying optimal treatment regimes in high-stakes medical contexts by developing a safe and interpretable framework that matches patients with similar characteristics to construct policies via interpolation, and applied it to seizure treatment in critically ill patients, finding that personalized strategies based on medical history and pharmacological features improve outcomes.
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.