Dual-channel knowledge attention for traditional Chinese medicine syndrome differentiation

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.