mlsanityData to 7 Oct 2026

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

arXiv 2409.11064 ↗First posted 17 Sep 2024cs.LG cs.ROcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 49 references that are in the map. First posted on arXiv 17 Sep 2024.

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References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  4. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  5. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  6. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  7. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  8. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  9. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  10. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 577
  11. 2017Gate-Variants of Gated Recurrent Unit (GRU) Neural Networkscited by 239
  12. 2021AutoFormer: Searching Transformers for Visual Recognitioncited by 186
6 more
  1. 2023TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecastingcited by 136
  2. 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 100
  3. 2023TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoderscited by 42
  4. 2024ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysiscited by 26
  5. 2024Generative Pretrained Hierarchical Transformer for Time Series Forecastingcited by 15
  6. 2023ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classificationcited by 7

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