HMF: A Hybrid Multi-Factor Framework for Dynamic Intraoperative Hypotension Prediction

• URL: http://arxiv.org/abs/2409.11064v1
• Date: Tue, 17 Sep 2024 10:46:41 GMT
• Title: HMF: A Hybrid Multi-Factor Framework for Dynamic Intraoperative Hypotension Prediction
• Authors: Mingyue Cheng, Jintao Zhang, Zhiding Liu, Chunli Liu, Yanhu Xie,
• Abstract summary: Intraoperative hypotension (IOH) prediction using Mean Arterial Pressure (MAP) is a critical research area with significant implications for patient outcomes during surgery. Existing approaches predominantly employ static modeling paradigms that overlook the dynamic nature of physiological signals. We introduce a novel Hybrid Multi-Factor (HMF) framework that reformulates IOH prediction as a blood pressure forecasting task.
• Score: 2.7807763048110337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intraoperative hypotension (IOH) prediction using Mean Arterial Pressure (MAP) is a critical research area with significant implications for patient outcomes during surgery. However, existing approaches predominantly employ static modeling paradigms that overlook the dynamic nature of physiological signals. In this paper, we introduce a novel Hybrid Multi-Factor (HMF) framework that reformulates IOH prediction as a blood pressure forecasting task. Our framework leverages a Transformer encoder, specifically designed to effectively capture the temporal evolution of MAP series through a patch-based input representation, which segments the input physiological series into informative patches for accurate analysis. To address the challenges of distribution shift in physiological series, our approach incorporates two key innovations: (1) Symmetric normalization and de-normalization processes help mitigate distributional drift in statistical properties, thereby ensuring the model's robustness across varying conditions, and (2) Sequence decomposition, which disaggregates the input series into trend and seasonal components, allowing for a more precise modeling of inherent sequence dependencies. Extensive experiments conducted on two real-world datasets demonstrate the superior performance of our approach compared to competitive baselines, particularly in capturing the nuanced variations in input series that are crucial for accurate IOH prediction.

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