可访问的中度至重度阻塞性睡眠呼吸暂停查工具,使用多维肥胖指标作为紧的表示
Xiaoyue Zhu1,2, Chenyang Li1,2, Xiaoting Wang1,2
1Department of Otolaryngology Head and Neck Surgery, Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
iScience
|February 21, 2025
概括
这项研究开发了使用肥胖指标的机器学习模型,以预测中度至严重的阻塞性睡眠呼吸暂停 (OSA). 光梯度增强机模型显示出高精度,有助于早期OSA检测.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 生物统计学 生物统计学
背景情况:
- 肥胖指标与脂肪和其分布有关.
- 评估中度至重度阻塞性睡眠呼吸暂停 (OSA) 风险可以从多维肥胖指标中受益.
- 自动化模型可以提高OSA风险评估的效率.
研究的目的:
- 开发和验证用于预测中度至重度OSA的机器学习模型.
- 利用多维肥胖指标作为OSA风险预测的特征.
- 为了比较各种机器学习算法在OSA检测中的性能.
主要方法:
- 在临床和社区数据集上训练并验证了后勤回归和五种机器学习算法.
- 采用19个肥胖指标作为模型的输入特征.
- 利用光梯度增强机 (LGB) 以其卓越的性能.
主要成果:
- 与其他算法相比,光梯度增强机 (LGB) 模型表现出卓越的校准和临床实用性.
- 在临床和社区环境中,LGB模型在预测中度至严重的OSA方面取得了相当大的准确性.
- 开发的模型有效地利用了19个肥胖指标进行预测.
结论:
- 机器学习,特别是LGB算法,可以使用多维肥胖指标准确预测中度至重度的OSA.
- 开发的模型显示了实际应用的可行性,可以有效地检测未被诊断的中度至重度OSA.
- 通过用户友好的界面部署该模型可以在临床和社区环境中增强OSA查.
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