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基于机器学习的非心脏手术心血管风险计算器

Nour Al Khatib1, Ali Chehab1, Hani Tamim2,3

  • 1Department of Electrical and Computer Engineering, American University of Beirut Maroun Semaan Faculty of Engineering and Architecture, Beirut, Lebanon.

Open heart
|January 7, 2026
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概括

使用LightGBM的机器学习模型准确地预测了50岁以上接受非心脏手术的患者的心血管风险. 这种工具有助于识别高风险个体,以获得更好的外科手术结果.

关键词:
生物统计学 生物统计学心肌梗塞的心脏病发作风险因素 风险因素这是一次中风.

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科学领域:

  • 心血管医学 心血管医学
  • 机器学习在医疗保健中的应用.
  • 手术风险评估手术风险评估

背景情况:

  • 全球4%的人口每年接受非心脏手术.
  • 30%的这些患者有心血管风险因素,30天死亡率为0.5%-2%.
  • 在这个人群中需要准确的心血管风险预测.

研究的目的:

  • 开发一个可解释的机器学习模型用于心血管风险评分.
  • 预测50岁以上接受非心脏手术的患者的风险.
  • 从手术日期到手术后30天评估风险.

主要方法:

  • 使用了NSQIP 2022数据集 (4,970,011名患者).
  • 主要终点定义为30天死亡,心肌梗塞,心脏骤停或中风.
  • 使用AUROC.训练和评估多个机器学习算法 (逻辑回归,天真贝叶斯,随机森林,提升树木).

主要成果:

  • 轻GBM实现了最高的AUROC,即0.9009 (95%CI:0.8889-0.9126).
  • 最好的模型确定了六个关键预测因素:手术类型,ASA分类,BUN,败血症,紧急手术和机械通风.
  • 该模型表现出强大的预测准确性和概括性.

结论:

  • 在这种情况下,LightGBM分类器是心血管风险评分的最佳选择.
  • 该模型有效地平衡了预测准确性和概括性.
  • 确定了非心脏手术患者心血管风险评估的关键因素.