培训基于机器学习的螺旋计参考方程:与GAMLSS和GLI参考方程进行比较
Walid Al-Qerem1, Anan Jarab2, Judith Eberhardt3
1Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, Jordan.
European clinical respiratory journal
|October 8, 2025
概括
机器学习 (ML) 螺旋计参考方程对约旦成年人来说是有前途的,比全球标准更准确. 这些ML模型为该地区的呼吸道诊断提供了更简单,更有效的替代方案.
科学领域:
- 呼吸系统医学 呼吸系统医学
- 生物统计学 生物统计学
- 机器学习应用 机器学习应用
背景情况:
- 精确的螺旋测量解释依赖于特定人群的参考方程.
- 现有的位置,规模和形状 (GAMLSS) 模型的一般化添加模型虽然有效,但可能很复杂.
- 需要更简单,更有效的替代方案,用于螺旋计参考方程.
研究的目的:
- 为了评估基于机器学习 (ML) 的螺旋计参考方程作为约旦成年人的替代方案.
- 将ML模型的效率与现有的GAMLSS和全球肺部倡议 (GLI) 方程进行比较.
- 评估ML衍生的螺旋测量规范的临床实用性.
主要方法:
- 使用现有数据集进行ML模型培训的横截面研究 (n=1,948).
- 开发了使用年龄和身高来预测FEV1,FVC和FEV1/FVC的ML模型.
- 对一个新队列 (n=487) 的健康,不吸烟的成年人进行了外部验证.
主要成果:
- ML模型显示了与约旦GAMLSS方程相比较的平均平方误差 (MSE).
- 与全球参考方程相比,ML模型显示MSE和z-score较低,接近于零.
- ML和约旦模型表现出卓越的校准,并正确地将所有健康个体归类为正常.
结论:
- ML衍生出的螺旋测量方程与约旦成年人观察到的数据非常一致.
- 在代表当地人口方面,ML模型的表现优于全球标准.
- 区域特定的参考方程,如使用ML开发的方程,对于准确的呼吸道诊断至关重要.
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