在胸痛患者中识别急性大动脉剖析的可解释机器学习方法
Shuangshuang Li1, Kaiwen Zhao2, Wen Li3
1School of Medicine, Tongji University, Shanghai 200092, China; Department of Vascular Surgery, the Third Affiliated Hospital of the Navy Medical University, Shanghai, 200433, China.
Annals of vascular surgery
|September 5, 2025
概括
这项研究开发了一种可解释的机器学习模型,以区分急性主动脉解剖 (AAD) 和其他胸痛原因. 该模型有效地识别了关键的生物标志物和临床特征,以更快地诊断AAD.
科学领域:
- 生物医学信息学
- 心血管医学
- 在医疗保健中的机器学习
背景情况:
- 准确及时诊断急性大动脉解剖 (AAD) 对患者的结果至关重要.
- 与急性心肌梗塞 (AMI),急性肺栓塞 (APE) 和腹主动脉动脉瘤 (AAA) 等突发胸痛的其他原因区分开来是一个临床挑战.
研究的目的:
- 开发和验证可解释的机器学习模型来识别患有AAD的患者.
- 使用常规实验室生物标志物和临床数据,区分AAD与其他引起胸痛的严重疾病.
主要方法:
- 使用了832名患者 (515名患有AAD) 的队列,数据分为训练和测试组.
- 采用LASSO回归用于变量选择和9个机器学习算法用于模型开发.
- 应用SHAP方法进行特征重要性分析和模型可解释性.
主要成果:
- 在LASSO回归中发现了八个重要的变量,包括N.L和年龄,用于AAD预测.
- 机器学习模型在测试组中达到0.72到0.77的曲线下面面积 (AUC).
- 随机森林模型在灵敏度,特异性和F1评分方面表现出强的表现.
结论:
- 一个可解释的机器学习模型可以有效地帮助区分AAD与胸痛的其他原因.
- 通过可解释的人工智能对关键特征的优先考虑有助于快速诊断和治疗AAD.
- 这种方法具有改善紧急情况下临床决策的巨大潜力.
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