针对心血管--代谢综合征相关冠状动脉心脏病的优化风险评估系统的开发,该系统基于表格式的先前数据配套网络
Shidian Zhu1,2, Hui Zhang1,2, Yanlin Liu3
1Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Jiangsu Joint International Laboratory of Animal-Derived Chinese Medicine and Functional Peptides, Nanjing University of Chinese Medicine, Nanjing, China.
Digital health
|September 18, 2025
概括
表格式先前数据配套网络 (TabPFN) 在小型医疗数据集中有效预测冠心病 (CHD) 风险. 这种新模型改进了心血管--代谢综合征 (CKM) 患者的传统方法.
科学领域:
- 人工智能在医学中的应用
- 心血管疾病风险预测预测
- 针对小型数据集的机器学习
背景情况:
- 心血管--代谢 (CKM) 综合征对冠心病 (CHD) 风险评估提出了复杂的挑战.
- 传统的机器学习 (ML) 方法往往在小的医学样本大小下扎.
- 表格式先前数据配套网络 (TabPFN) 是一种新兴的人工智能技术,具有医学数据分析的潜力.
研究的目的:
- 为了比较TabPFN与传统ML算法的性能,用于预测CKM-CHD患者的冠状动脉狭窄.
- 开发和验证使用TabPFN用于小样本医疗数据的新型风险预测模型.
- 在CKM-CHD患者队列中确定冠状动脉狭窄的关键风险因素.
主要方法:
- 这项研究遵循了TRIPOD+AI的指导方针,分析了两个医院分支机构的296名住院患者的数据.
- 数据被分为培训 (n=160),内部验证 (n=54) 和外部验证 (n=82) 集.
- 综合最小绝对收缩和选择操作员回归确定了风险因素;TabPFN和八个ML算法构建了预测模型,使用标准指标,校准,决策和学习曲线进行评估.
主要成果:
- 确定了五个重要的危险因素:冠状动脉计算机断层扫描血管学,2型糖尿病,甘油三糖指数,体重指数和绝对淋巴细胞计数.
- 在小样本中,TabPFN表现出优于传统模型的性能,AUC值为0.922 (训练),0.857 (内部验证) 和0.815 (外部验证).
- 优化模型将冠状动脉计算机断层扫描血管学假阴性率降低了4.9%,开发了一个用户友好的Shiny计算器.
结论:
- 在医学数据分析方面,TabPFN显著有前途,特别是在小样本场景中.
- 开发的CKM-CHD风险模型为临床决策提供了宝贵的支持.
- 建议通过更大规模的多中心前性研究进行进一步验证,以优化模型.
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