一种用于术中低血压早期预警的混合多因素网络与动态序列建模
A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative Hypotension
AI总结：
提出混合多因素网络HMF，将术中低血压预测建模为动态序列任务，分解趋势与季节成分并用补丁Transformer编码，引入对称归一化处理分布漂移，在临床数据集上显著优于基线。
摘要：
术中低血压（IOH）的预测对于利用过去的生理信号至关重要，因为IOH可能导致器官灌注不足，并显著增加严重并发症和死亡的风险。然而，当前方法往往依赖静态建模，忽视了生理信号中固有的复杂时间依赖性和非平稳性。我们提出了一种混合多因素（HMF）网络，将IOH预测建模为动态序列预测任务，明确捕获时间依赖性和生理非平稳性。我们将信号动态表示为多元时间序列，并将其分解为趋势和季节性成分，从而能够分别建模长期变化和周期性变化。每个成分通过基于补丁的Transformer进行编码，以平衡计算效率和特征表示。为了解决信号演变带来的分布漂移问题，我们引入了一种对称归一化机制。在公开和真实临床数据集上的实验表明，HMF显著优于竞争性基线方法。我们希望HMF能为IOH预测提供新的见解，并最终促进更安全的手术护理。我们的代码可在https://github.com/Mingyue-Cheng/HMF获取。
英文摘要：
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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