MissHyper：在缺失引导的超图预测中恢复临床同步性
MissHyper: Restoring Clinical Synchronicity in Missingness-Guided Hypergraph Forecasting
AI总结：
研究临床不规则多元时间序列预测中的预传播表示瓶颈，提出MissHyper模型，通过恢复共时间戳上下文、增强事件、聚合记录和自适应融合证据来改进预测，在多数据集上优于基线，证明事件初始化对稀疏临床预测的重要性。
AI中文摘要：
临床不规则多元时间序列不仅受生理动态影响，还受测量过程影响。在以事件为中心的模型中，共时间戳结构可能过早扁平化。我们研究了这种预传播表示瓶颈，并通过在消息传递开始前恢复共时间戳上下文来解决。我们提出了MissHyper，一种具有预传播同步性恢复的缺失引导超图预测模型。它用局部支持密度线索增强每个事件，聚合共时间戳记录以恢复患者状态上下文，并使用缺失引导门自适应融合节点特定证据与恢复的上下文。在多个数据集上，MissHyper在多步预测中取得一致增益，优于强超图基线。消融实验表明快照恢复、自适应融合和支持密度编码都有贡献，指出事件初始化是稀疏临床预测的关键设计轴。
英文摘要：
Clinical irregular multivariate time series are shaped not only by physiological dynamics but also by the measurement process that determines when and what to observe. In event-centric models, however, co-timestamp structure can be flattened too early: measurements acquired at the same timestamp are embedded as isolated nodes, leaving local patient-state context unavailable until later message-passing layers. We study this pre-propagation representation bottleneck and address it by restoring co-timestamp context before message passing begins. We propose MissHyper, a missingness-guided hypergraph forecasting model with pre-propagation synchronicity restoration. MissHyper augments each event with a local support-density cue, aggregates co-timestamp records to recover patient-state context, and uses a missingness-guided gate to adaptively fuse node-specific evidence with the recovered context. Across PhysioNet 2012, MIMIC-III, and MIMIC-IV, MissHyper achieves consistent gains in multi-step forecasting and outperforms a strong hypergraph baseline. These results suggest that improving event initialization can benefit sparse clinical forecasting without requiring a redesigned downstream propagation architecture. Ablations indicate that snapshot restoration, adaptive fusion, and support-density encoding all contribute, pointing to event initialization as a critical design axis for sparse clinical forecasting.
