通过可解释的机器学习提高热中风预测的准确性:一个多中心数据驱动的方法
Qingbo Zeng1,2, Xingping Deng1, Longping He1
1The 908th Hospital of Chinese PLA Logistic Support Force, Nanchang, China.
PeerJ
|November 19, 2025
概括
这项研究开发了一种可解释的机器学习模型,使用临床数据预测热中风. 梯度增强机器模型表现出强的性能,确定了肌酸激酶-MB作为早期热中风检测的关键预测因子.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 公共卫生监督 公共卫生监督
背景情况:
- 热中风是一个关键的公共卫生问题,死亡率高.
- 热中风的准确及时诊断对于有效的患者管理至关重要.
- 现有的诊断方法可能会从增强的预测工具中受益.
研究的目的:
- 开发和验证可解释的机器学习模型,用于热中风预测.
- 利用临床和实验室数据预测热中风风险.
- 加强热中风患者的早期识别和管理.
主要方法:
- 一个梯度增强机 (GBM) 模型是使用24家医院 (2021-2022) 的数据开发的.
- 模型性能使用接收器操作特征曲线 (AUROC) 下的面积和校准图进行了评估.
- 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:
- GBM 模型在培训数据上获得了 0.971 的 AUROC,在验证数据上获得了 0.836 的 AUROC.
- 肌酸激酶 (CK) -MB被确定为GBM模型中最重要的预测因子.
- 决策曲线分析表明,GBM模型具有实质性的净收益.
结论:
- 开发的机器学习模型显示了强大的热中风预测能力.
- 这种可解释的模型可以帮助临床医生识别和管理风险患者.
- 这些发现支持将ML工具集成到临床实践中,用于热中风监测.
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