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：近期统计与强化学习方法显著提升了患者护理策略。然而，这些方法在高风险场景中面临重大挑战，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要求。本研究提出一种安全且可解释的框架，用于识别最优治疗方案。该方法通过匹配具有相似医学与药理学特征的患者，借助插值法构建最优策略。我们开展全面的模拟研究，验证该框架在复杂场景下识别最优策略的能力。最终，我们将该方法应用于危重症患者癫痫发作治疗方案研究。研究结果强有力地支持基于患者病史及药理学特征的个性化治疗策略。值得注意的是，研究发现：对于轻度短暂癫痫发作患者减少药物剂量，同时对重症监护室中经历剧烈癫痫发作的患者采取激进治疗，能够带来更有利的临床结局。