Traditional Chinese Medicine (TCM) plays an important role in global medical practices. Syndrome differentiation (SD) is a key step in the diagnosis and treatment of TCM, which involves a comprehensive analysis of patient clinical information. However, the process of SD involves a complex mapping of various symptoms and signs to their corresponding syndrome types. It requires models to have strong non-linear feature modeling capabilities, emphasizing the semantic associations and feature differences between syndrome types. Additionally, the models must be able to effectively distinguish rare syndrome types, thereby enhancing both accuracy and interpretability. To this end, a multi knowledge enhanced framework combined with Kolmogorov-Arnold, named SD-MKEK, is proposed. SD-MKEK effectively captures the complex relationships between syndrome types and symptoms through a hierarchical structure, enabling accurate SD. In the feature extraction phase, multiple knowledge enhancement module is designed to extract context-sensitive features and significantly enhance the discriminability of the features through a label-guided mechanism. In the decision-making phase, a cross-attention mechanism is combined with the Kolmogorov-Arnold classifier, and a learnable activation function is used to better capture the complex relationships in high-dimensional data. Experimental results on the multi-disease multi-syndrome TCM-SD dataset show that the performance of SD-MKEK is superior to existing state-of-the-art baselines. Experiments on the single-disease multi-syndrome COPD-SD dataset also demonstrate the effectiveness of the proposed algorithm. This study can effectively perform the task of identifying TCM syndromes and has important value in promoting the deep integration of traditional medicine with modern computing technologies.
Keywords: Attention mechanism; Contrastive learning; Kolmogorov–Arnold; Representation learning; Syndrome differentiation.
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