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Updated: Sep 12, 2025

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机器学习模型的开发和验证,用于使用常规血液生物标志物和衍生指标预测易受伤害的动脉斑块:对与性别相关的风险模式的洞察

Yimin E¹, Zhichao Yao¹, Maolin Ge²

¹Department of Vascular Surgery, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, No. 26 Daoqian Street, Jiangsu, Suzhou, China.

Cardiovascular diabetology

|August 10, 2025

概括

使用常规血液检测的机器学习模型准确地预测了易受伤害的动脉斑块. 该研究强调了预防中风的生物标志物风险的性别特异性差异.

科学领域:

心血管医学心血管医学
医疗信息学医疗信息学
生物标志物发现发现

背景情况:

早期发现易受伤害的动脉斑块对于预防中风至关重要.
机器学习提供了一种新的方法,可以使用可访问的数据来预测斑块的脆弱性.

研究的目的:

开发和验证一种机器学习模型,用于使用常规血液检测和衍生指数预测易受伤害的动脉斑块.
调查与斑块脆弱性的各种生物标志物水平相关的性别特异性风险模式.

主要方法:

1701名患者的回顾性分析,进行了带超声波和易受损伤的斑块识别.
提取和分析30个实验室变量和衍生指数 (例如,TyG,AIP,NLR).
培训和评估五种机器学习模型,使用SHAP分析解释最佳模型,并通过RCS建模探索性别差异.

主要成果:

随机森林模型实现了高预测性能 (AUC=0.847).
关键预测因素包括性别,年龄,纤维素,NLR,肌素,禁食血糖,UHR,TyG,SIRI和淋巴细胞计数.
在诸如年龄,UHR,TyG和SIRI之类的生物标志物的预测影响上观察到显著的性别特异性差异.

结论:

随机森林模型整合了常规血液标记物和衍生指数,有效地预测了易受伤害的动脉斑块.
调查结果强调,由于性别差异性生物标志物影响,需要进行性别特异性风险评估.

关键词:

血液中的生物标志物机器学习机器学习在SHAP分析中,我们分析了SHAP.性差异性差异性差异性差异性差异性差异性差异性易受伤害的頸動脈斑塊.

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