近日，研究中心硕士生杨毅的一篇论文“Syndrome differentiation of Traditional Chinese Medicine via multiple knowledge enhancement with Kolmogorov-Arnold Theorem”被Artificial Intelligence in Medicine（JCR一区，影响因子6.2）录用。(论文链接地址：论文链接地址：https://doi.org/10.1016/j.artmed.2026.103396)。

论文摘要：中医（Traditional Chinese Medicine, TCM）在全球医疗实践中占据重要地位。辨证（Syndrome Differentiation, SD）作为中医诊疗的核心环节，需对患者临床信息进行综合分析。然而，辨证过程涉及症状体征与对应证型的复杂映射，要求模型具备强大的非线性特征建模能力，重点关注证型间的语义关联与特征差异；同时需有效识别罕见证型，以提升诊断的准确性与可解释性。为此，本文提出一种融合柯尔莫哥洛夫-阿诺尔德（Kolmogorov-Arnold）理论的多知识增强框架——SD-MKEK。该框架通过分层结构有效捕捉证型与症状间的复杂关系，实现精准辨证。在特征提取阶段，设计多知识增强模块以提取上下文敏感特征，并通过标签引导机制显著增强特征的判别性；决策阶段则将交叉注意力机制与柯尔莫哥洛夫-阿诺尔德分类器结合，采用可学习激活函数以更好地捕捉高维数据中的复杂关联。在多病多证数据集TCM-SD上的实验结果表明，SD-MKEK性能优于现有主流基线模型；在单病多证数据集COPD-SD上的实验进一步验证了算法的有效性。本研究可高效完成中医证型识别任务，对推动传统医学与现代计算技术的深度融合具有重要价值。

Abstract: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.