MambaSleepCVD for prediction of long term cardiovascular outcomes from polysomnography
· 2026-09-09 · 原文
DOI：10.64898/2026.09.08.26361626v1?rss=1预印本：medRxiv开放获取：绿色OA
Long-term cardiovascular disease (CVD) risk prediction remains a major global clinical priority. This study investigates how much prognostic information full-night polysomnography (PSG) carries after explicitly controlling demographic confounding, and whether combining specialized unimodal representations with continuous fusion mechanisms can effectively leverage it. We adopt a modular framework based on Coupled Mamba for simultaneous cross-modal and temporal fusion, capturing long-range dependencies and dynamic interactions between physiological modalities throughout sleep. With this capability we assess whether specialized supervised representations from task-specific unimodal encoders offer prognostic value comparable to task-agnostic multimodal self-supervised pre-training. Validated o
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1. 人话版
Long-term cardiovascular disease (CVD) risk prediction remains a major global clinical priority.
This study investigates how much prognostic information full-night polysomnography (PSG) carries after explicitly controlling demographic confounding, and whether combining specialized unimodal representations with continuous fusion mechanisms can effectively leverage it.
2. 领域脉络
We adopt a modular framework based on Coupled Mamba for simultaneous cross-modal and temporal fusion, capturing long-range dependencies and dynamic interactions between physiological modalities throughout sleep.
3. 机制拆解
With this capability we assess whether specialized supervised representations from task-specific unimodal encoders offer prognostic value comparable to task-agnostic multimodal self-supervised pre-training.
4. 证据与数字
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5. 反例与边界
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6. 跨领域连接与意外收获
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7. 可复用方法
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8. 术语表
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