Cross-Sample Augmented Test-Time Adaptation for Personalized Intraoperative Hypotension Prediction

• URL: http://arxiv.org/abs/2512.15762v1
• Date: Fri, 12 Dec 2025 08:02:37 GMT
• Title: Cross-Sample Augmented Test-Time Adaptation for Personalized Intraoperative Hypotension Prediction
• Authors: Kanxue Li, Yibing Zhan, Hua Jin, Chongchong Qi, Xu Lin, Baosheng Yu,
• Abstract summary: Intraoperative hypotension poses significant surgical risks, but accurate prediction remains challenging due to patient-specific variability.<n>We propose CSA-TTA, a novel Cross-Sample Augmented Test-Time Adaptation framework that enhances training by incorporating hypotension events from other individuals.<n>We evaluate the proposed CSA-TTA on both the VitalDB dataset and a real-world in-hospital dataset by integrating it with state-of-the-art time series forecasting models.
• Score: 45.67071315035565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intraoperative hypotension (IOH) poses significant surgical risks, but accurate prediction remains challenging due to patient-specific variability. While test-time adaptation (TTA) offers a promising approach for personalized prediction, the rarity of IOH events often leads to unreliable test-time training. To address this, we propose CSA-TTA, a novel Cross-Sample Augmented Test-Time Adaptation framework that enhances training by incorporating hypotension events from other individuals. Specifically, we first construct a cross-sample bank by segmenting historical data into hypotensive and non-hypotensive samples. Then, we introduce a coarse-to-fine retrieval strategy for building test-time training data: we initially apply K-Shape clustering to identify representative cluster centers and subsequently retrieve the top-K semantically similar samples based on the current patient signal. Additionally, we integrate both self-supervised masked reconstruction and retrospective sequence forecasting signals during training to enhance model adaptability to rapid and subtle intraoperative dynamics. We evaluate the proposed CSA-TTA on both the VitalDB dataset and a real-world in-hospital dataset by integrating it with state-of-the-art time series forecasting models, including TimesFM and UniTS. CSA-TTA consistently enhances performance across settings-for instance, on VitalDB, it improves Recall and F1 scores by +1.33% and +1.13%, respectively, under fine-tuning, and by +7.46% and +5.07% in zero-shot scenarios-demonstrating strong robustness and generalization.

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