基于机器学习方法,针对心力衰竭患者的液体体积状态检测模型.
Haozhe Huang1, Jing Guan1, Chao Feng2
1School of Mathematics, Tianjin University, Tianjin, 300350, China.
Heliyon
|January 15, 2025
概括
机器学习有效地检测心力衰竭患者的液体体积状况,帮助医生精确管理. 这种方法可以改善心力衰竭恶化的早期检测和治疗.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 液体体积异常是心力衰竭恶化的主要原因.
- 目前用于评估流体状态的方法通常是低效,缓慢和昂贵的.
- 准确的体积状态评估对于有效的心力衰竭管理至关重要.
研究的目的:
- 为心力衰竭患者开发和验证早期液体体积检测模型.
- 使用机器学习来分层患者的液体体积状态.
- 提高心力衰竭中液体状况评估的准确性和效率.
主要方法:
- 利用了2056名心力衰竭患者的数据集,具有97个医学特征.
- 应用最小冗余性最大相关性 (mRMR) 用于特征选择.
- 开发并评估了使用ROC AUC,校准曲线和其他指标的四种机器学习分类模型.
- 外部验证的模型包括186名心力衰竭患者.
- 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
主要成果:
- 为模型开发选择了30个关键特性.
- 在模型和数据集中实现了ROC曲线下的面积 (AUC) 从0.64到0.77不等.
- 外部验证表明模型性能与AUC在0.64和0.74.7之间.
- SHAP分析提供了对物流回归模型的全球解释.
结论:
- 机器学习模型在检测心力衰竭患者的液体体积状态方面表现出有效性.
- 这些模型可以作为辅助诊断的宝贵工具.
- 这些发现支持机器学习的使用,以便为心力衰竭患者提供精确,定制的管理策略.
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