开发一种可解释的机器学习模型,用于早期预测系统性红斑狼的心血管参与
Zixian Deng1, Huadong Liu1, Feng Chen2
1Department of Cardiology, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, 518020, People's Republic of China.
Journal of inflammation research
|July 8, 2025
概括
这项研究开发了一种可解释的机器学习模型,用于预测系统性红斑狼 (SLE) 中的心脏参与. 渐变增强机器确定了关键预测因素,如关节炎和高血压,用于早期评估SLE患者的风险.
科学领域:
- 心脏病学 心脏病学
- 类风湿病学 类风湿病学
- 人工智能的人工智能
背景情况:
- 心血管疾病是系统性红斑狼 (SLE) 患者死亡的主要原因.
- 在SLE中早期发现心脏并发症对于改善患者的预后和结果至关重要.
- 这项研究的重点是确定预测因子,并开发SLE中心脏参与的预测模型.
研究的目的:
- 确定与SLE患者心脏参与相关的关键临床和生物因素.
- 开发和验证可解释的机器学习 (ML) 模型,用于预测SLE心脏并发症风险.
- 加强高风险SLE个体的早期识别和管理.
主要方法:
- 在2000年至2021年期间住院的1,023名SLE患者的回顾性分析.
- 使用了三种特征选择技术 (随机森林,LASSO,XGBoost) 来识别预测变量.
- 训练并测试了七个ML算法,并根据其性能和可解释性选择了梯度增强机 (GBM).
主要成果:
- 在平均3737天的随访期间,18.77%的患者患有心脏病.
- 确定了七个关键预测因素:关节炎,高血压,HDL-C,LDL-C,总胆固醇,CRP和ESR.
- GBM模型实现了0.748的AUC和0.779.9的准确性.
结论:
- 这项研究提出了第一个可解释的ML模型,用于预测SLE中心脏参与风险.
- 开发的GBM模型证明了最佳性能,并为临床医生提供了对决策过程的见解.
- 该模型有助于早期识别心脏并发症风险较高的SLE患者.
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