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基于机器学习的可解释的健康评分自动非线性计算评分系统和用于预测外科手术期间中风的应用:回顾性研究

Mi-Young Oh1, Hee-Soo Kim2, Young Mi Jung3

  • 1Department of Neurology, Sejong General Hospital, Sejong General Hospital, Bucheon-si, Republic of Korea.

Journal of medical Internet research
|March 19, 2025
PubMed
概括

新的可解释的自动非线性计算对健康 (EACH) 评分的评分系统有效地预测了外科手术前的中风. 这种可解释的机器学习工具在真实数据中显示出比传统方法更高的性能.

关键词:
基于ML的模型基于ML的模型.非线性计算是一种非线性计算.申请申请表 申请表 申请表计算得分系统计算得分系统有效性 有效性.效率 效率 效率 效率 效率 效率 效率可以解释性的解释性.机器学习是机器学习.没有心脏的非心脏.非心脏手术的手术.病人的病人的病人的病.在外科手术期间的外科手术.在外科手术期间的中风.现实世界的数据数据.危险的风险 危险的风险风险工具是风险工具.球队的成绩 球队的成绩 球队的成绩一次性中风中风中风中风中风手术 手术 手术 手术 手术 手术 手术工具 工具 工具 工具

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习
  • 预测分析是一种预测分析.

背景情况:

  • 机器学习 (ML) 模型为捕捉数据中的复杂,非线性交互提供了先进的功能.
  • 然而,许多ML模型的"黑子"性质限制了它们的临床解释性和采用性.
  • 需要ML工具来平衡预测能力与透明度.

研究的目的:

  • 开发和验证一种新的,可解释的基于机器学习的评分系统,用于预测外科手术期间的中风.
  • 为了提高模型理解,利用夏普利增量解释 (SHAP) 值.
  • 创建一个高效和临床适用的风险评估工具.

主要方法:

  • 开发了可解释的健康 (EACH) 框架的自动非线性计算评分系统.
  • 利用了CatBoost模型,确定了关键的预测特征,并使用了SHAP图表来确定关键的变化点.
  • 规范了EACH分数,并对来自不同机构的独立患者队伍的表现进行了验证.

主要成果:

  • 在一个大队列 (n=38,737) 中,EACH得分实现了0.829的曲线下面面积 (AUC),用于外科手术期间中风预测.
  • 外部验证证实其优越的预测准确性 (AUC=0.784) 与传统得分 (AUC=0.528) 和替代ML模型 (AUC=0.564) 相比.
  • 在地理和时间上不同的数据集上表现出强大的性能.

结论:

  • 每个评分代表了一个精确和可解释的基于机器学习的风险预测工具.
  • 它的有效性在现实世界的临床数据中得到验证,优于现有方法.
  • 每个都为预测外科手术后中风风险提供了有前途的进展.