针对多变量结果基于偏好的治疗效果的目标主动学习

中文摘要 AI 辅助

治疗效果传统上基于单一主要结果来证明。然而，临床决策通常需要考虑多种结果，在预期收益与潜在风险之间进行权衡。这些结果被赋予的相对价值因患者而异。给定一个关于结果概况的偏好规则，可以定义并估计治疗效果和最优策略。这样的规则在实践中很少可用：它本身必须从结果概况的成对比较中估计，而从临床专家那里收集这些比较成本高昂。我们提出了一个主动学习框架，用于选择要查询哪些比较。标准准则最大化关于偏好规则本身所获得的信息。我们转而针对感兴趣的量，并选择最能减少由学习规则诱导的治疗效果和最优策略不确定性的查询。在偏好规则的高斯过程模型下，我们推导出该准则的闭式近似。在基于一个具有13个临床结果的帕金森病队列构建的半合成数据上，我们的准则在相同查询预算下实现了比现有准则更低的治疗效果估计误差和更低的策略遗憾。

英文摘要

Treatment efficacy is traditionally demonstrated on the basis of a single primary outcome. However, clinical decision-making usually requires consideration of multiple outcomes, balancing expected benefits against potential risks. The relative value assigned to these outcomes varies substantially from one patient to another. Given a preference rule over outcome profiles, treatment effects and optimal policies can be defined and estimated. Such a rule is rarely available in practice: it must itself be estimated from pairwise comparisons of outcome profiles, which are costly to collect from clinical experts. We propose an active learning framework that selects which comparisons to query. Standard criteria maximize the information gained on the preference rule itself. We instead target the quantities of interest, and select the query that most reduces uncertainty on the treatment effect and on the optimal policy induced by the learned rule. Under a Gaussian process model of the preference rule, we derive a closed-form approximation of this criterion. On semi-synthetic data built from a Parkinson's disease cohort with 13 clinical outcomes, our criterion achieves lower treatment effect estimation error and lower policy regret than existing criteria at equal query budget.

发表机构

• Theremia
• Paris Brain Institute (ICM)（巴黎脑科学研究所（ICM））
• Sorbonne Université（索邦大学）
• Inserm（法国国家健康与医学研究院）
• CNRS（法国国家科学研究中心）
• AP-HP（巴黎公共援助医院）
• Inria（法国国家信息与自动化研究所）
• Université de Montpellier（蒙彼利埃大学）
• Université Paris Cité（巴黎西岱大学）
• Centre for Research in Epidemiology and Statistics (CRESS)（流行病学与统计学研究中心（CRESS））

机构由 AI 辅助整理，请以论文原文为准。