机器学习模型用于检测年轻成年人面具高血压
Brendyn Miller1, Samuel J Coeyman2, Annemarie Wentzel3,4
1Institute for Regenerative Medicine, Wake Forest University, Winston-Salem, NC, United States.
Frontiers in physiology
|December 3, 2025
概括
机器学习模型可以使用临床数据预测掩盖高血压 (MHT),改善早期检测. 这种方法有助于管理心血管疾病风险,特别是在资源有限的环境中.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因,高血压 (HT) 贡献很大.
- 蒙面高血压 (MHT),在诊所内血压正常,但在诊所外血压高,增加心血管疾病风险,并且往往未被诊断出来.
- 目前的诊断工具,如ABPM和HBPM的可访问性和可行性限制.
研究的目的:
- 开发和验证机器学习 (ML) 模型,使用单次诊断临床数据预测MHT.
- 解决MHT的诊断挑战,特别是在资源有限的环境中.
主要方法:
- 利用了来自非洲PREDICT研究的数据,包括来自南非年轻人队伍的临床,生物标志物,身体组成和身体活动指标.
- 采用ML模型,包括LASSO特征选择和极端梯度提升,用于MHT预测.
- 使用精度和ROC AUC分数评估模型性能.
主要成果:
- 一个结合LASSO特征选择和极端梯度增强的ML模型实现了0.83准确度和0.86ROC AUC.
- 该模型主要依赖于四个关键特征:缩血压,体重,缩时的左心室质量和脱水二二酸盐水平.
- 这种预测框架表明了早期MHT识别的潜力,减少了对资源密集型监测的依赖.
结论:
- 基于ML的MHT预测为早期检测和干预提供了一种可行的方法.
- 这一策略可以帮助缓解MHT进展和相关的心血管风险,特别是在服务不足的地区.
- 将计算技术集成到临床实践中对于应对高血压等全球健康挑战至关重要.
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