A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative Hypotension

• URL: http://arxiv.org/abs/2409.11064v2
• Date: Sun, 23 Feb 2025 07:06:38 GMT
• Title: A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative Hypotension
• Authors: Mingyue Cheng, Jintao Zhang, Zhiding Liu, Chunli Liu, Yanhu Xie,
• Abstract summary: Intraoperative hypotension (IOH) prediction using past physiological signals is crucial.<n>Existing IOH prediction methods often rely on static modeling.<n>We propose a Hybrid Multi-Factor (HMF) network that models IOH prediction as a dynamic sequence forecasting problem.
• Score: 2.7807763048110337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intraoperative hypotension (IOH) prediction using past physiological signals is crucial, as IOH can lead to inadequate organ perfusion, increasing the risk of severe complications and mortality. However, existing IOH prediction methods often rely on static modeling, overlooking the complex temporal dependencies and non-stationary nature of physiological signals. In this paper, we propose a Hybrid Multi-Factor (HMF) network that models IOH prediction as a dynamic sequence forecasting problem, explicitly capturing temporal dependencies and physiological non-stationarity.. Specifically, we formalize physiological signal dynamics as a sequence of multivariate time series, and decompose them into trend and seasonal components, enabling distinct modeling of long-term and periodic variations. For each component, we employ a patch-based Transformer encoder to extract representative features with the concern of computational efficiency and representation quality. Furthermore, to mitigate distributional drift arising from the evolving signals, we introduce a symmetric normalization mechanism. Extensive experiments on both a publicly available dataset and a private dataset collected from real-world hospital settings demonstrate that our approach significantly outperforms competitive baselines. We hope HMF offers a new perspective on IOH prediction and further enhances surgical safety. Our code is open-sourced and available at \footnote{https://github.com/Mingyue-Cheng/HMF}.

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