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.

翻译：近年来的统计与强化学习方法显著推进了患者护理策略的发展。然而，在高风险场景下，这些方法面临重大挑战，包括数据缺失、固有随机性以及对可解释性和患者安全性的关键需求。本研究实现了一个安全且可解释的框架，用于识别最优治疗方案。该方法通过对具有相似医学和药理学特征的患者进行匹配，从而通过插值构建最优策略。我们开展了一项综合性模拟研究，以展示该框架在复杂场景中识别最优策略的能力。最终，我们将该方法应用于研究危重症患者癫痫发作的治疗方案。研究结果强烈支持基于患者病史和药理学特征的个性化治疗策略。值得注意的是，我们识别出对轻度且短暂癫痫发作患者减少药物剂量，同时对重症监护病房中经历剧烈癫痫发作的患者采取积极治疗，能够带来更有利的临床结局。