Individualized treatment rules (ITRs) aim to optimize healthcare by tailoring treatment decisions to patient-specific characteristics. Existing methods typically rely on either interpretable but inflexible models or highly flexible black-box approaches that sacrifice interpretability; moreover, most impose a single global decision rule across patients. We introduce the Locally Interpretable Individualized Treatment Rule (LI-ITR) method, which combines flexible machine learning models to accurately learn complex treatment outcomes with locally interpretable approximations to construct subject-specific treatment rules. LI-ITR employs variational autoencoders to generate realistic local synthetic samples and learns individualized decision rules through a mixture of interpretable experts. Simulation studies show that LI-ITR accurately recovers true subject-specific local coefficients and optimal treatment strategies. An application to precision side-effect management in breast cancer illustrates the necessity of flexible predictive modeling and highlights the practical utility of LI-ITR in estimating optimal treatment rules while providing transparent, clinically interpretable explanations.

翻译：个体化治疗规则旨在通过根据患者特定特征定制治疗决策来优化医疗保健。现有方法通常依赖于可解释但缺乏灵活性的模型，或高度灵活但牺牲可解释性的黑盒方法；此外，大多数方法对所有患者施加单一的全局决策规则。本文提出局部可解释个体化治疗规则方法，该方法结合灵活的机器学习模型以准确学习复杂治疗结果，并利用局部可解释近似构建针对特定个体的治疗规则。LI-ITR采用变分自编码器生成真实的局部合成样本，并通过可解释专家混合模型学习个体化决策规则。仿真研究表明，LI-ITR能准确还原真实的个体局部系数和最优治疗策略。在乳腺癌精准副作用管理中的应用案例，证明了灵活预测建模的必要性，并凸显了LI-ITR在估计最优治疗规则同时提供透明、临床可解释说明方面的实用价值。