针对生存数据删失问题，提出可感知删失的树形强化学习方法。
Censoring-Aware Tree-Based Reinforcement Learning for Estimating Dynamic Treatment Regimes with Censored Outcomes
- 融合AIPW与删失感知机制改进树形强化学习。
- 在癫痫数据集上，RMST和决策准确率优于ASCL方法。
- 适合临床研究中需处理删失数据的个性化治疗策略设计。
动态治疗方案（DTRs）为根据患者特征进行序列化治疗决策提供系统方法，尤其适用于关注生存结局的临床场景。本文提出一种新型框架——删失感知树形强化学习（CA-TRL），以应对估计最优DTR时面临的删失数据复杂性。通过在传统树形强化学习基础上引入增广逆概率加权（AIPW）和删失感知修正，CA-TRL能够从观察性数据中学习稳健且可解释的治疗策略。我们在大规模模拟和真实世界应用中验证了其有效性，使用SANAD癫痫数据集显示，该方法在受限平均生存时间（RMST）和决策准确性等关键指标上优于近期提出的ASCL方法。本研究推动了多样化医疗环境中个性化、数据驱动治疗策略的发展。
原文摘要 · Abstract (English)
Dynamic Treatment Regimes (DTRs) provide a systematic approach for making sequential treatment decisions that adapt to individual patient characteristics, particularly in clinical contexts where survival outcomes are of interest. Censoring-Aware Tree-Based Reinforcement Learning (CA-TRL) is a novel framework to address the complexities associated with censored data when estimating optimal DTRs. We explore ways to learn effective DTRs, from observational data. By enhancing traditional tree-based reinforcement learning methods with augmented inverse probability weighting (AIPW) and censoring-aware modifications, CA-TRL delivers robust and interpretable treatment strategies. We demonstrate its effectiveness through extensive simulations and real-world applications using the SANAD epilepsy dataset, where it outperformed the recently proposed ASCL method in key metrics such as restricted mean survival time (RMST) and decision-making accuracy. This work represents a step forward in advancing personalized and data-driven treatment strategies across diverse healthcare settings.
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