| 引用本文: |
夏鑫,李丽,崔宏达,吕怡,彭清华.中医辨证可计算化的演进逻辑与现代启示——基于朱文锋“证素辨证”体系的学术述评[J].湖南中医药大学学报,,():[点击复制] |
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| 中医辨证可计算化的演进逻辑与现代启示——基于朱文锋“证素辨证”体系的学术述评 |
| 夏鑫,李丽,崔宏达,吕怡,彭清华 |
| (湖南中医药大学, 湖南 长沙 410208;湖南省中医药研究院, 湖南 长沙 410000) |
| 摘要: |
| 辨证论治的客观化、标准化与可计算化，是中医现代化进程中的关键瓶颈。朱文锋教授创造性提出以“证素”为核心的辨证理论，将复杂的证候网络降维拆解为“病位证素”与“病性证素”的有限组合，成功构建了“症-证素-证名”的结构化推理路径。本文首先梳理近11年“证素+人工智能”领域的研究趋势;继而从知识工程与数学模型双重维度解构其运算逻辑，并结合复杂病案演算与数学公式强化论证;同时，针对该体系存在的线性加权缺陷、边界模糊及静态截面计算等局限开展学术审视。最后，通过与当代深度学习路径的比较，提出前瞻性建议:引入图神经网络（GNN）深化中医病机复杂网络表达，依托多中心真实世界数据客观校验诊断参数，并区分循环神经网络（RNN）与隐马尔可夫模型（HMM）的适用场景，完善时序动态辨证建模，以期为当代中医诊疗知识的智能计算研究提供深层次的方法学参考。 |
| 关键词: 中医诊断学 证素辨证 辨证可计算化 人工智能 朱文锋 |
| DOI： |
| 投稿时间：2026-06-27 |
| 基金项目:国家自然科学基金项目（82274588）；湖南省中医药科研课题（B20260019）。 |
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| Evolutionary logic and modern implications of computable TCM pattern identification: an academic review based on ZHU Wenfeng's pattern element identification system |
| XIA Xin,LI Li,CUI Hongda,LYU Yi,PENG Qinghua |
| (Hunan University of Chinese Medicine, Changsha, Hunan 410208, China;Hunan Academy of Chinese Medicine, Changsha, Hunan 410000, China) |
| Abstract: |
| The objectivization, standardization, and computability of pattern identification and treatment constitute the key bottlenecks in TCM modernization. Professor ZHU Wenfeng creatively proposed the pattern identification theory centered on "pattern elements (Zhengsu)", which reduced the complexity of pattern networks by decomposing them into a finite combination of "disease-location pattern elements" and "disease -nature pattern elements," and successfully constructed a structured reasoning pathway of "symptom-pattern element-pattern name." This paper first reviews the research trends in the "pattern elements + artificial intelligence" field over the past 11 years; it then deconstructs the computational logic of this system from the dual dimensions of knowledge engineering and mathematical models, and reinforces the argumentation through complex medical case analyses and mathematical formulas; meanwhile it conducts an academic review of the system's limitations, such as linear weighting defects, boundary ambiguity, and static cross-sectional computation. Finally, by comparing it with contemporary deep learning approaches, this paper proposes forward-looking recommendations including introducing Graph Neural Networks (GNN) to deepen the complex network representation of TCM pathogenesis, objectively validating diagnostic parameters based on multicenter real-world data, and distinguishing the applicable scenarios for Recurrent Neural Networks (RNN) and Hidden Markov Models (HMM) to perfect time-series dynamic pattern identification modeling. This study aims to provide in-depth methodological references for the research on intelligent computation of contemporary TCM diagnostic and treatment knowledge. |
| Key words: TCM diagnostics pattern element identification computable pattern identification artificial intelligence ZHU Wenfeng |
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