在老年人中亚临床肝脏和功能障碍的性别特定分类使用机器学习和细胞因子分析
Lvtao Zeng1,2, Li Zhang1,2, Sijia Li1,3
1The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Digital health
|February 9, 2026
概括
机器学习和细胞因子分析有效地检测老年人的肝脏和脏问题. 这种方法增强了早期疾病检测和预防性医疗保健策略.
科学领域:
- 老年学是一门学科.
- 生物标志物发现发现
- 计算生物学 计算生物学
背景情况:
- 老龄化人口面临肝脏和功能障碍的风险增加.
- 早期发现这些异常对于有效管理至关重要.
- 细胞因子概况为器官功能评估提供了潜在的生物标志物.
研究的目的:
- 开发和验证机器学习模型,以区分老年人群中的肝脏和功能异常.
- 确定与这些异常相关的关键细胞因子生物标志物.
- 加强早期检测技术,以改善医疗保健结果.
主要方法:
- 在760名参与者的数据上利用了后勤回归,随机森林,拉索回归,极端梯度增强和支持向量机器.
- 使用ELISA测量细胞因子水平,并进行标准肝脏和功能测试.
- 应用合成少数群体过量采样技术 (SMOTE) 来解决培训数据中的类不平衡.
主要成果:
- 拉索回归实现了高的功能曲线下面面积 (AUC) 值 (0.895男性,0.940女性).
- 后勤回归表明肝功能表现强 (AUC为0.918男性,0.794女性).
- 确定了特定的细胞因子 (例如,VCAM-1,GDF-15) 作为肝脏和脏异常的关键区分因子,SMOTE改善了模型性能.
结论:
- 机器学习与细胞因子分析相结合,是早期检测老年人肝脏和脏异常的强大工具.
- 这种综合方法对推进预防性医疗保健具有重大前景.
- 已识别的细胞因子生物标志物可以帮助开发有针对性的诊断策略.
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