用动态序列建模预测手术低血压，提升早期预警精度。
A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative Hypotension
- 将生理信号分解为趋势与周期分量，分别用补丁Transformer编码。
- 在公开与真实临床数据上，性能显著优于现有方法。
- 适合关注手术安全与实时预警系统的临床研究者。
利用术中生理信号预测术中低血压（IOH）至关重要，因其可能导致器官灌注不足，显著增加严重并发症和死亡风险。然而，现有方法多依赖静态建模，忽视了生理信号复杂的时序依赖性和非平稳特性。本文提出混合多因子（HMF）网络，将IOH预测建模为动态序列预测任务，显式捕捉时序依赖与生理信号非平稳性。将信号动态表示为多变量时间序列，并分解为趋势与季节成分，分别建模长期与周期性变化。每类成分采用基于补丁的Transformer编码，兼顾计算效率与特征表达。针对信号演化引起的分布漂移，引入对称归一化机制。在公开及真实临床数据集上的实验表明，HMF显著优于多个基线模型。代码已开源，期待为IOH预测提供新思路，推动更安全的外科手术护理。
原文摘要 · Abstract (English)
Intraoperative hypotension (IOH) prediction using past physiological signals is crucial, as IOH may lead to inadequate organ perfusion and significantly elevate the risk of severe complications and mortality. However, current methods often rely on static modeling, overlooking the complex temporal dependencies and the inherently non-stationary nature of physiological signals. We propose a Hybrid Multi-Factor (HMF) network that formulates IOH prediction as a dynamic sequence forecasting task, explicitly capturing both temporal dependencies and physiological non-stationarity. We represent signal dynamics as multivariate time series and decompose them into trend and seasonal components, enabling separate modeling of long-term and periodic variations. Each component is encoded with a patch-based Transformer to balance computational efficiency and feature representation. To address distributional drift from evolving signals, we introduce a symmetric normalization mechanism. Experiments on both public and real-world clinical datasets show that HMF significantly outperforms competitive baselines. We hope HMF offers new insights into IOH prediction and ultimately promotes safer surgical care. Our code is available at https://github.com/Mingyue-Cheng/HMF.
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