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机器学习模型的开发,用于预测心脏康复的有效性和坚持.

Konstantina-Helen Tsarapatsani, Vasilis D Tsakanikas, Boris Schmitz

    IEEE journal of biomedical and health informatics
    |December 8, 2025
    PubMed
    概括

    机器学习模型可以预测心脏康复 (CR) 的有效性和坚持. 随机森林和物流回归模型显示出有希望的结果,使个性化患者护理和CR计划的改善结果成为可能.

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    科学领域:

    • 心脏病学 心脏病学
    • 生物医学信息学 生物医学信息学
    • 数据科学数据科学数据科学

    背景情况:

    • 心脏康复 (CR) 对于心脏事件后的恢复至关重要.
    • 在CR程序有效性和患者坚持的变化可能会导致低于最佳的结果.
    • 需要预测模型来个性化CR干预.

    研究的目的:

    • 开发和评估用于预测CR程序有效性的机器学习 (ML) 模型.
    • 开发和评估用于预测CR程序遵守的ML模型.
    • 创建一个系统来个性化可视化患者对CR的反应.

    主要方法:

    • 来自西班牙CR单位的1448名参与者的回顾性队列研究.
    • 数据预处理包括清理,规范化,归算和特征选择.
    • 开发和验证ML模型 (随机森林,物流回归) 使用交叉验证,根据AUC,特异性,灵敏性和均衡准确性进行评估.

    主要成果:

    • 随机森林模型实现了0.789的AUC来预测CR有效性.
    • 后勤回归模型实现了0.757的AUC,用于预测CR坚持.
    • 用SHAP图表进行变量分析,并开发了一个二维评分系统.

    结论:

    • 机器学习模型可以有效地预测CR程序的有效性和遵守性.
    • 个性化预测可实现量身定制的患者管理,并可能改善CR结果.
    • 开发的评分系统为可视化患者特异性CR反应提供了一种新的方法.