使用机器学习开发伊拉克特有的螺旋计参考方程
Walid Al-Qerem1, Alaa Alsajri2, Anan Jarab3
1Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, Jordan.
Future science OA
|November 5, 2025
概括
机器学习为伊拉克成年人创造了新的螺旋计参考方程,超过了全球标准. 这些特定于性别的方程提高了伊拉克人口的肺功能测试准确度.
科学领域:
- 肺部医学 肺部医学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 螺旋测量对于诊断和管理呼吸系统疾病至关重要.
- 准确的解释需要特定种群的参考方程.
- 现有的全球方程可能无法准确地代表伊拉克人的肺功能.
研究的目的:
- 为健康的伊拉克成年人制定性别特定的螺旋计参考方程.
- 使用机器学习 (ML) 进行方程开发.
- 将ML衍生的方程与全球肺功能倡议 (GLI) 2012年和2022年的方程进行比较.
主要方法:
- 一项对3,959名健康,不吸烟的伊拉克成年人 (≥18岁) 的横截面研究.
- 根据ATS/ERS指导方针进行螺旋计.
- 根据年龄和身高训练了五个ML模型;梯度增强机 (GBM) 被选为最佳.
- 使用RMSE,R2和z-score校准评估的性能.
主要成果:
- 与其他ML模型和GLI方程相比,GBM模型表现出优越的性能.
- 对于女性来说,R2是0.4473 (FEV1) 和0.4519 (FVC);对于男性来说,是0.3509 (FEV1) 和0.3674 (FVC).
- GLI方程低估了肺体积;GBM预测显示了良好的校准,平均z-score接近零.
结论:
- 机器学习衍生出的螺旋测量方程为伊拉克成年人提供了更高的准确性和校准.
- 这些新的方程为这种人群中的螺旋测量解释提供了一个更合适的工具.
- 这项研究强调了ML在制定局部肺功能参考标准方面的有用性.
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