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English(EN) Learning transferable human physiology from two million hours of sleep with SleepFM-2

SleepFM-2 模型从 200 万小时睡眠数据中学习可迁移的人类生理学

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 SleepFM-2，这是一个新颖的睡眠基础模型，在近 30 万次睡眠记录的超过两百万小时的多模态生理数据上进行了训练。与前代模型 SleepFM 相比，该模型在疾病预测和睡眠评分方面表现出显著的改进。SleepFM-2 在不同传感器（包括可穿戴设备）之间表现出强大的迁移能力，甚至能够捕捉通常未在标准多导睡眠图摘要中反映的睡眠质量的主观方面。 AI

影响 该模型可以利用可穿戴设备上易于获取的睡眠数据来增强疾病预测和健康监测。

排序理由 该集群描述了一篇关于新颖睡眠生理学基础模型的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

SleepFM-2 模型从 200 万小时睡眠数据中学习可迁移的人类生理学

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该集群描述了一篇关于新颖睡眠生理学基础模型的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rahul Thapa, Christopher Sun, William Theodor Lehn-Schioler, Sophia Claire Kivelson, Umaer Hanif, Hyatt Moore IV, Harrison G. Zhang, Hafsa Ahmed, Marcus Dige, Niels R. Lorenzen, Elisabeth Roxane M. Heremans, Adrien Specht, Ulysse Gimenez, Robin Guillard,… ·

    从两百万小时睡眠数据中学习可迁移的人体生理学，借助 SleepFM-2

    arXiv:2609.06849v1 Announce Type: new Abstract: Sleep provides a nightly window into health by capturing coordinated activity across the brain, heart, muscles and respiratory system. We introduce SleepFM-2, a sleep foundation model developed and evaluated on 282,511 polysomnograp…

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