开发和验证可解释的机器学习模型,用于早期查动脉样硬化
1National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China.
Journal of translational medicine
|May 28, 2023
概括
机器学习模型使用常规健康数据有效地查动脉样硬化 (CAS). 年龄,性别和胆固醇水平是早期CAS检测和预防的关键指标.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 心血管疾病研究研究
背景情况:
- Carotid 动脉样硬化 (CAS) 是中风的重要危险因素和一个主要的公共卫生问题.
- 早期检测CAS对于中风预防策略至关重要.
- 常规健康检查指标为广泛的CAS查提供了潜在的资源.
研究的目的:
- 开发和验证用于CAS早期查的机器学习 (ML) 模型.
- 为了利用常规健康检查数据进行CAS识别.
- 在CAS查中评估ML模型的性能和可解释性.
主要方法:
- 利用了来自中国东北部 (2018-2019) 的69,601份健康检查记录.
- 在数据集上训练并测试了10个ML算法,包括渐变增强决策树 (GBDT).
- 在接收器运行特征曲线 (auROC) 下使用的面积和SHapley添加式解释 (SHAP) 用于性能评估和可解释性.
主要成果:
- GBDT模型表现出高性能,其auROC为0.860 (内部) 和0.851 (外部验证).
- 通过SHAP分析发现的关键预测因素包括年龄,性别和非高密度脂蛋白胆固醇.
- 在糖尿病患者或65岁以上的人群中观察到较低的负预测值.
结论:
- 开发的ML模型显示了使用常规健康指标进行有效CAS查的前景.
- 这些模型有可能在不同人群中应用于CAS预防.
- 可解释性分析强调了CAS风险的关键人口统计和临床因素.
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