AI 中文总结
针对生理时间序列补全中通用模型的不足，提出CAIR两阶段框架，在多数据集多缺失机制下均实现最优性能，且能同时兼顾重建误差与临床负担指标。
AI 中文摘要
生理时间序列（动脉血压、血糖等）的补全对于解决临床数据中普遍存在的缺失问题至关重要。然而现代补全方法在该领域表现不佳：一项近期基准测试发现，在存在现实间隙的真实临床信号上，简单线性插值的性能优于所有学习型补全器。我们表明，这反映了通用补全器所忽略的生理缺失的两个特性：间隙可能出现在信号临床极值而非典型值时，且间隙长度可轻易跨越数个数量级。为此，我们提出了课程感知的插值后优化（CAIR），一种用于生理时间序列补全的两阶段框架。我们的核心动机是学习一条粗略的基础曲线，然后反复将其修正至符合生理实际，而非单次通过预测间隙。因此，CAIR将双向GRU插值器与Transformer优化器相结合，后者在三次连续迭代中修正自身的估计值，在与信号无关的宽范围随机间隙课程下联合训练。我们按间隙长度和缺失机制（MCAR、MAR、NMAR）对补全器进行评估，而非采用单一平均值，CAIR在连续血糖监测（AI-READI）和重症监护动脉血压（MIMIC-III）的所有机制下均为最准确的模型。其与最强基线的优势随难度提升而增大，从MCAR下的9%到值相关缺失下的19%，而通用学习型补全器在该场景下最弱。我们进一步表明，仅低重建误差无法恢复临床医生所依赖的负担指标：与CAIR误差匹配的插值无法保留这些指标，恢复这些指标的补全器准确性则低得多，而CAIR是唯一在两个维度上均排名最优的模型之一。
英文摘要
Imputing physiological time series (arterial blood pressure, blood glucose, etc.) is essential for addressing the missingness that pervades clinical data. Yet modern imputation methods perform poorly in this domain: a recent benchmark found that simple linear interpolation outperformed every learned imputer on real-world clinical signals with realistic gaps. We show that this reflects two properties of physiological missingness that generic imputers ignore: gaps may occur when the signal is clinically extreme rather than typical, and gap lengths can easily span orders of magnitude. To this end, we introduce Curriculum-Aware Interpolate-then-Refine (CAIR), a two-stage framework for physiological time-series imputation. Our key motivation is to learn a coarse base curve and then repeatedly correct it toward physiological realism, rather than predict a gap in a single pass. Consequently, CAIR couples a bidirectional-GRU interpolator with a Transformer refiner that corrects its own estimate over three successive passes, trained jointly under a broad, signal-agnostic random-gap curriculum. We evaluate imputers stratified by gap length and missingness mechanism (MCAR, MAR, NMAR) rather than by a single average, and CAIR is the most accurate under every mechanism on continuous glucose monitoring (AI-READI) and arterial pressure in intensive care (MIMIC-III). Its margin over the strongest baseline grows with difficulty, from 9% under MCAR to 19% under value-dependent dropout, where generic learned imputers are weakest. We further show low reconstruction error alone does not recover the burden metrics clinicians act on: interpolants matching CAIR's error fail to preserve those metrics, imputers that recover them are far less accurate, and CAIR alone ranks among the best on both axes.
