让黑箱模型的治疗决策变透明，为每位患者定制可解释的治疗方案。
Locally Interpretable Individualized Treatment Rules for Black-Box Decision Models
- 用变分自编码器生成局部合成数据，构建个体化可解释专家模型。
- 模拟实验显示能精准恢复真实患者的最优治疗策略和局部系数。
- 适合需要个性化且可解释治疗决策的临床场景，如乳腺癌副作用管理。
个体化治疗规则（ITRs）旨在通过根据患者特征定制治疗决策来优化医疗效果。现有方法通常依赖于可解释但僵化的模型，或高度灵活但不可解释的黑箱方法；此外，大多数方法对所有患者采用单一全局决策规则。本文提出局部可解释个体化治疗规则（LI-ITR）方法，结合灵活的机器学习模型准确学习复杂的治疗结果，同时利用局部可解释的近似方法构建针对个体的治疗规则。LI-ITR采用变分自编码器生成真实的局部合成样本，并通过可解释专家的混合模型学习个体化决策规则。模拟研究显示，LI-ITR能准确恢复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表明，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的同时提供透明、临床可解释解释的实际价值。
原文摘要 · Abstract (English)
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.
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