中医杂志

中医辨证诊断中症状信息的模糊性易在缺乏约束的症证直接推理过程中被放大，影响智能辨证的精确性与稳定性。传统中医疾病概念虽具历史合理性，但其内涵与外延的结构性模糊难以在智能建模中承担高稳定性先验知识的功能。相较之下，现代医学疾病基于客观检测与可量化指标，具备边界相对明确、标准可复现的特征。提出在病证结合模式下，以现代医学疾病作为结构性先验变量，重构病、症、证三层信息关系，通过疾病约束实现四诊信息的有效筛选与推理空间压缩。结合人工智能多模态融合、知识图谱等技术，构建先验知识与临床数据联合驱动的智能辨证模式，为提升辨证精确性、实现中医诊疗智能化提供可行路径。

The ambiguity of symptom information in traditional Chinese medicine(TCM) syndrome differentiation can be amplified in the direct reasoning process from symptoms to syndromes in the absence of constraints, which affects the accuracy and stability of intelligent syndrome differentiation. TCM disease concepts, while historically rational, are structurally ambiguous in both their connotation and extension, making it difficult to serve as stable prior knowledge in intelligent modeling. In contrast, modern medical diseases, based on objective testing and quantifiable indicators, have relatively clear boundaries and reproducible standards. This study proposes a disease-syndrome combination model, adopting modern medical diseases as structural prior variables to reconstruct the hierarchical relationships among disease, symptoms, and syndromes. By applying disease constraints, effective screening of information from the four examinations and compressing the reasoning space are achieved. Furthermore, by integrating artificial intelligence technologies, such as multimodal fusion and knowledge graphs, an intelligent syndrome differentiation model driven by both prior knowledge and clinical data is constructed, providing a feasible path to enhance the accuracy of syndrome differentiation and realize the intelligentization of TCM diagnosis and treatment.