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Updated: Jan 10, 2026

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
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Published on: December 6, 2016

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预测,预防和个性化医学的多变量数据驱动框架,用于新发性阻塞性睡眠呼吸暂停患者的长期心房风险

Jian Zhu1, Yijun Wang2, Shoupeng Duan3

  • 1Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 230000 Anhui China.

The EPMA journal
|November 28, 2025
PubMed
概括

这项研究开发了一种XGBoost模型,用于预测阻塞性睡眠呼吸暂停 (OSA) 患者的心房动 (AF) 风险,提供个性化医学见解. 该模型实现了高精度,有助于早期风险评估和管理.

关键词:
呼吸暂停呼吸暂停指数心房动是一种心房动.自主神经系统的自主神经系统.评估健康风险 评估健康风险个性化的患者概况.机器学习是机器学习.多变量数据是多变量数据.阻塞性睡眠呼吸暂停症是什么范式的转变发生了变化.预测性预防性个性化医学 (PPPM / 3PM)普遍存在的与睡眠有关的呼吸障碍.

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