常见机器学习技术在中医证候诊断中的应用进展

The Application Progress of Common Machine Learning Techniques in Traditional Chinese Medicine Syndrome Diagnosis

摘要: 在中医学中，辨证诊断占据着核心地位，它依托于对患者症状、体征的全面评估，以及舌象、脉象等关键信息的分析，以准确界定疾病的本质。辨证诊断过程面临的挑战包括证型识别的主观差异、证型变异的复杂度以及中医四诊信息的非标准化问题，这些都极大地提升了对医生专业知识和经验的需求，使得对新手医生而言，实现“精准辨证”显得尤为艰难。随着人工智能技术的不断进步，其与多学科的交叉融合日益加深，并被广泛利用于探索中医证候的诊断研究之中。这种融合为中医的辨证诊断研究带来了新颖的方法与工具。在此背景下，我通过文献回顾，对中医证候研究中应用的主要机器学习技术进行了分类和总结评价，分析了各类机器学习技术的优缺点，并讨论了利用机器学习进行中医研究所遇到的问题及其在未来的应用潜力，旨在为中医证候诊断领域内机器学习算法的深入研究与应用奠定参考基础。

Abstract: In Traditional Chinese Medicine (TCM), evidence-based diagnosis occupies a central position, relying on a comprehensive assessment of the patient’s symptoms and signs, as well as the analysis of key information, such as the tongue and pulse, in order to accurately define the nature of the disease. Challenges to the diagnostic process include the subjective differences in pattern recognition, the complexity of pattern variation, and the non-standardization of the four diagnostic information in TCM, all of which greatly increase the need for doctors’ expertise and experience, making accurate pattern recognition particularly difficult for novice doctors. With the continuous progress of AI technology, its cross-fertilization with multiple disciplines is deepening, and has been widely utilized in diagnostic research to explore TCM evidence. This integration has brought novel methods and tools for TCM diagnostic research. In this context, I classify and summarize the main machine learning techniques applied in TCM diagnostic studies through a literature review, analyze the advantages and disadvantages of each type of machine learning technique, and discuss the problems encountered in using machine learning for TCM research and its potential for future application, with the aim of laying a reference foundation for the in-depth research and application of machine learning algorithms in the field of TCM diagnostic diagnosis.