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

2024-09-17

AI Summary

This work addresses the challenge of modeling non-stationary physiological signals and complex temporal dependencies in early intraoperative hypotension (IOH) prediction, where conventional static models fall short. We propose a dynamic multivariate time-series modeling paradigm. Methodologically, we introduce a novel trend-cycle dual-path decomposition architecture, incorporate symmetric normalization to mitigate distributional shift, and design a lightweight block-wise Transformer encoder to balance computational efficiency and representational capacity. Our framework employs dynamic sequence forecasting for end-to-end IOH risk modeling. Evaluated on both public benchmarks and real-world private hospital datasets, the model achieves reliable IOH prediction 5–15 minutes in advance, with a maximum AUC improvement of 6.2% over state-of-the-art baselines. The implementation is publicly available.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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}.

Problem

• Predicts Intraoperative Hypotension dynamically
• Captures temporal dependencies in signals
• Handles physiological non-stationarity effectively

Innovation

• Hybrid Multi-Factor network
• Dynamic sequence modeling
• Patch-based Transformer encoder