Liu Boting, Huang Hengqiu, Liu Xiaoman, Zhao Juntao, Mo Jiean
School of Mathematics and Computer Science, Guangxi Minzu Normal University, Chongzuo, 532200, China.
Sci Rep. 2025 Apr 18;15(1):13487. doi: 10.1038/s41598-025-96404-w.
With the rapid advancement of Natural Language Processing (NLP) technologies, the application of NLP to enable intelligent syndrome differentiation in Traditional Chinese Medicine (TCM) has become a popular research focus. However, TCM texts contain numerous obscure characters and specialized terminologies, which existing methods struggle to effectively extract, leading to lower accuracy in syndrome differentiation. To address this, we propose a dual-channel knowledge-attention model for TCM syndrome differentiation. The model utilizes the ZY-BERT, a large pre-trained model in the TCM domain, to extract vector representations of TCM texts. A dual-channel network, comprising an improved Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network, is employed to capture both critical local information and global patterns in TCM texts. Additionally, an attention mechanism is introduced to enhance the model's ability to learn syndrome-related knowledge, integrating syndrome definition knowledge to improve the model's ability to differentiate complex syndromes. Experiments conducted on a publicly available TCM syndrome differentiation dataset demonstrate that the proposed model achieves an accuracy of 84.01%, representing an 1.75% improvement in accuracy compared to the best baseline model.
随着自然语言处理（NLP）技术的快速发展，将NLP应用于中医智能辨证已成为热门研究焦点。然而，中医文本包含大量生僻字和专业术语，现有方法难以有效提取，导致辨证准确率较低。为解决这一问题，我们提出了一种用于中医辨证的双通道知识注意力模型。该模型利用中医领域的大型预训练模型ZY-BERT来提取中医文本的向量表示。采用由改进的卷积神经网络（CNN）和长短期记忆（LSTM）网络组成的双通道网络，以捕捉中医文本中的关键局部信息和全局模式。此外，引入注意力机制以增强模型学习证候相关知识的能力，整合证候定义知识以提高模型辨别复杂证候的能力。在公开可用的中医辨证数据集上进行的实验表明，所提出的模型准确率达到84.01%，与最佳基线模型相比，准确率提高了1.75%。
