English(EN) Online Survival Analysis: A Bandit Approach under Cox PH Model

在线生存分析：基于Cox PH模型的Bandit方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-22 07:52

研究人员开发了一种新颖的在线生存分析Bandit方法，将Cox比例风险模型整合到序贯决策中。该方法解决了交错进入、延迟反馈和右删失等挑战，通过适应现有的Bandit算法，在新数据出现时优化治疗方案。模拟和SEER癌症数据的实验表明，该方法能有效学习近乎最优的治疗策略。 AI

影响为生存分析中的序贯决策引入了一个新框架，有望改善医疗环境中的治疗优化。

排序理由这是一篇研究论文，介绍了一种在线学习环境中生存分析的新方法。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Rui Song · 2026-04-22 07:52

在线生存分析：基于Cox PH模型的Bandit方法

Survival analysis is a widely used statistical framework for modeling time-to-event data under censoring. Classical methods, such as the Cox proportional hazards (Cox PH) model, offer a semiparametric approach to estimating the effects of covariates on the hazard function. Despit…