预测模型的研究 带 - 关节脉冲波浪速度:基于机器学习算法
Minghui Chen1, Jing Xiong2, Moran Li2
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Journal of clinical hypertension (Greenwich, Conn.)
|March 18, 2025
概括
一个新的机器学习模型预测了带-股骨脉冲波速率 (cf-PWV),这是一个关键的动脉硬度测量方法,使用手腕-脚脉冲波速率 (baPWV) 和临床数据. 这种方法提供了一种非侵入性的方法来评估心血管风险.
科学领域:
- 心血管研究的心血管研究.
- 生物医学工程 生物医学工程
- 机器学习在医疗保健中的应用
背景情况:
- 关节-关节脉冲波速 (cf-PWV) 是动脉硬和心血管风险的重要指标.
- 直接测量cf-PWV是复杂的,在常规的临床实践中往往无法实现.
- 需要可访问的临床参数和非侵入性测量来估计cf-PWV.
研究的目的:
- 开发cf-PWV的预测模型,使用脚脉冲波速度 (baPWV) 和其他可访问的临床数据.
- 为了实现cf-PWV的非侵入性估计,用于早期心血管风险评估.
- 验证预测模型的性能及其与死亡风险的关联.
主要方法:
- 利用了上海北部社区 (2013-2022) 的数据.
- 在特征选择和模型开发中使用Pearson相关性和线性回归.
- 应用了考克斯的比例危险模型和梯度提升与SHAP分析用于验证和解释性.
主要成果:
- 开发了一种机器学习模型,以低RMSE和R2值显示出良好的预测性能.
- 该模型显示,预测的cf-PWV与死亡风险之间存在显著的关联.
- 创建了一个分类模型来识别高cf-PWV值.
结论:
- 机器学习,使用baPWV和临床数据,可以有效地预测cf-PWV.
- 这种非侵入性方法有助于cf-PWV估计,有助于临床决策.
- 预测的高cf-PWV值要求进一步精确测量和积极的健康管理.
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