基于机器学习的模型,用于预测患有阻塞性睡眠呼吸暂停,白天过度嗜睡的1型麻醉症患者
Yuanhang Pan1, Di Zhao1, Xinbo Zhang1
1Department of Neurology, Xijing Air Force Medical University, Xi'an, People's Republic of China.
Nature and science of sleep
|June 5, 2024
概括
机器学习模型可以帮助在阻塞性睡眠呼吸暂停 (OSA) 患者中检测1型麻醉症 (NT1). 渐变增强机模型在识别NT1风险因素,如发病年龄和睡眠指标方面表现出卓越的表现.
科学领域:
- 睡眠医学 睡眠医学
- 医疗保健中的人工智能
- 临床诊断 临床诊断 临床诊断
背景情况:
- 过度的白天嗜睡 (EDS) 在阻塞性睡眠呼吸暂停 (OSA) 和1型麻醉症 (NT1) 中很常见.
- 在患有OSA的患者中,NT1经常被忽视.
- 机器学习 (ML) 之前没有用于NT1诊断.
研究的目的:
- 开发ML预测模型,以便在OSA患者中早期识别并发性NT1.
- 帮助非睡眠专家临床医生识别具有NT1.1高概率的患者.
主要方法:
- 在三个睡眠中心收集了246名OSA患者的临床数据.
- 开发和评估了使用LASSO回归进行特征选择的9个ML模型.
- 使用AUC,校准曲线和DCA评估模型性能;用SHAP解释模型.
主要成果:
- 梯度增强机 (GBM) 模型的性能优于其他ML模型.
- 由GBM模型识别的关键特征包括发病年龄,四肢运动指数,睡眠延迟,NREM阶段2和OSA严重程度.
结论:
- 开发了一种可行的基于ML的查模型,用于在OSA患者中早期检测NT1.
- 建议在更广泛的临床环境中进一步验证.
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