TPC-GCN:深度学习用于传统中医药中的脉冲模式分类
Hui Li1, Yuetang Li1, Zhidong Zhang1
1State key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument, North University of China, Taiyuan, 030051, Shanxi, China.
Medical engineering & physics
|September 9, 2025
概括
这项研究使用先进的数据增强和新型图形卷积网络 (GCN) 增强了传统中医 (TCM) 脉冲诊断. 该方法显著提高了用于客观诊断的TCM脉冲模式分类的准确性.
科学领域:
- 计算医学是一种计算医学.
- 传统中国医学 (TCM) 是一种
- 医疗保健中的人工智能
背景情况:
- 脉冲诊断是TCM诊断的基石,它依赖于复杂的脉冲特征.
- 对脉冲模式的准确分类对于TCM的客观化和标准化至关重要.
- 现有的方法在处理脉冲数据的复杂性和变异性方面面临挑战.
研究的目的:
- 为准确和客观的TCM脉冲模式分类开发一种增强的方法.
- 用先进的机器学习技术提高诊断模型的稳定性和准确性.
- 为TCM中脉冲信号分析提供新的计算方法.
主要方法:
- 用一种增强的SMOTE (合成少数人过量采样技术) 方法来增强数据.
- 实现了多域特征提取,并构建了图形数据结构以进行深入分析.
- 设计了一个多通道轻量级图形卷积网络 (GCN) 具有注意重量的融合.
主要成果:
- 拟议的GCN模型在脉冲数据集上实现了高性能指标.
- 获得了91.68%的准确性,平均F1分数,回忆和精度均为92%.
- 在脉冲分类准确性和模型稳定性方面显著改进.
结论:
- 增强的SMOTE和GCN方法有效地提高了TCM脉冲分类的准确性.
- 该研究验证了客观TCM诊断所提出的方法的有效性.
- 为脉冲信号分类研究提供了新的计算视角和方法.
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