发表机构
Higher School of Artificial Intelligence Technologies; Peter the Great St.Petersburg Polytechnic University（人工智能技术高等学院; 彼得大帝圣彼得堡理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于注意力的 Surv-IPTB 模型，将生存数据的 IPTB 估计转化为二分类问题，通过成对患者比较处理右删失数据，在合成非线性数据集上优于 T-learner、S-learner 等基线模型，可用于个性化治疗获益评估。
AI 中文摘要
本研究提出了一种新颖的基于注意力的框架，用于在生存分析场景中估计个体治疗获益概率（Individual Probability of Treatment Benefit，IPTB）。所提出的模型名为 Surv-IPTB，可直接量化特定患者在接受治疗与接受对照治疗时获得更长生存时间的概率。我们将 IPTB 估计重新表述为二分类问题，利用治疗组与对照组之间的成对患者比较。该框架通过不精确概率表示法对右删失观测值进行合理处理，其中不确定的治疗效果由区间值概率表征。具有可学习查询-键变换的注意力机制能够灵活、数据驱动地聚合成对比较，同时为删失案例学习软分类概率。我们在具有复杂非线性结构的合成数据集（包括螺旋形、钟形和圆形特征空间）上进行了大量实验，结果表明，我们的方法在不同删失率和治疗效果强度下均保持稳健性能。该模型始终优于配备随机生存森林、Cox 比例风险模型和 Beran 估计器的元学习器基线（T-learner 和 S-learner），尤其是在传统方法表现显著退化的具有挑战性的非线性场景中。这些结果确立了所提出的基于注意力的框架为生存场景中个性化治疗获益评估的可扩展且统计合理的解决方案。实现该模型的代码已公开提供。
英文摘要
This work presents a novel attention-based framework for estimating the Individual Probability of Treatment Benefit (IPTB) in survival analysis contexts. The proposed model, called Surv-IPTB, directly quantifies the probability that a specific patient will experience extended survival time under treatment versus control. We reformulate IPTB estimation as a binary classification problem, leveraging pairwise patient comparisons across treatment and control cohorts. The framework incorporates a principled handling of right-censored observations through imprecise probability representations, where uncertain treatment effects are characterized by interval-valued probabilities. An attention mechanism with learnable query-key transformations enables flexible, data-driven aggregation of pairwise comparisons, while simultaneously learning soft class probabilities for censored cases. Through extensive experiments on synthetic datasets with complex nonlinear structures, including spiral, bell-shaped, and circular feature spaces, we demonstrate that our approach maintains robust performance across varying censoring rates and treatment effect strengths. The model consistently outperforms meta-learner baselines (T-learner and S-learner) equipped with random survival forests, Cox proportional hazards, and Beran estimators, particularly in challenging nonlinear scenarios where conventional methods exhibit significant degradation. The results establish the proposed attention-based framework as a scalable and statistically principled solution for personalized treatment benefit assessment in survival settings. The code implementing the model is publicly available.
