时间序列反事实推断用于时间临床数据：解决时间旅行者困境
Sequential Counterfactual Inference for Temporal Clinical Data: Addressing the Time Traveler Dilemma
AI总结：
本文提出顺序反事实框架，用于时间序列临床数据，解决时间旅行者困境，揭示心肾连锁反应的生物合理临床洞察。
摘要：
反事实推断使临床医生能够提出关于患者结果的'如果'问题，但标准方法假设特征独立性和同时可修改性——这些假设被纵向临床数据所违反。我们引入了顺序反事实框架，通过区分不可变特征（慢性诊断）和可控特征（实验室值）来尊重电子健康记录中的时间依赖性，并建模干预如何随时间传播。应用于2,723名新冠患者（383例长期新冠心力衰竭病例，2,340个匹配对照组），我们证明在朴素方法下，38-67%的慢性病患者需要生物上不可能的反事实。我们识别出一个心肾连锁反应（CKD->AKI->HF），每一步的相对风险分别为2.27和1.19，展示了时间传播，这表明顺序但非朴素的反事实能够捕捉到。我们的框架将反事实解释从'如果这个特征不同的话？'转变为'如果早些时候进行了干预，那会如何传播？'——产生基于生物学合理性的临床可操作见解。
英文摘要：
Counterfactual inference enables clinicians to ask "what if" questions about patient outcomes, but standard methods assume feature independence and simultaneous modifiability -- assumptions violated by longitudinal clinical data. We introduce the Sequential Counterfactual Framework, which respects temporal dependencies in electronic health records by distinguishing immutable features (chronic diagnoses) from controllable features (lab values) and modeling how interventions propagate through time. Applied to 2,723 COVID-19 patients (383 Long COVID heart failure cases, 2,340 matched controls), we demonstrate that 38-67% of patients with chronic conditions would require biologically impossible counterfactuals under naive methods. We identify a cardiorenal cascade (CKD -> AKI -> HF) with relative risks of 2.27 and 1.19 at each step, illustrating temporal propagation that sequential -- but not naive -- counterfactuals can capture. Our framework transforms counterfactual explanation from "what if this feature were different?" to "what if we had intervened earlier, and how would that propagate forward?" -- yielding clinically actionable insights grounded in biological plausibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
