中医证候辨识中机器学习模型应用现况与未来发展对策OA
Current status and future development strategies of machine learning in traditional Chinese medicine syndrome identification models
中医辨证是一个复杂的、非线性的思维过程.从庞大繁杂的临床数据中辨析其与证候间的复杂关系,明晰中医诊疗专家的辨证规律,学习其辨证过程,有利于中医辨证的传承与发展.传统辨证方式存在忽视证候复杂性、异病同证难以统一等困境.精准医学及智能医学时代的到来为中医证候诊断研究带来了新的思路与方法.文章对机器学习在中医证候辨识模型中的优势及相关研究进行梳理和阐述,在此基础上进一步分析现有研究中的问题,提出合理使用特征工程以实现精准化模型构建、多模态客观化信息源构建联合辨证模型等发展对策,以期能为未来证候辨识模型研究提供参考.
Traditional Chinese medicine(TCM)syndrome identification is a complex,non-linear thinking process.Analysing the complex relationship between the large volume of complex clinical data and the syndromes,clarifying the diagnostic rules of TCM experts and learning the process of identification can contribute to the inheritance and development of TCM syndrome identification.The traditional way of diagnosis faces the dilemma of neglecting the complexity of syndromes,and it is difficult to unify different diseases with the same syndrome.The arrival of the era of precision medicine and intelligent medicine has brought new ideas and methods to the study of TCM syndrome identification.This paper reviews and elaborates the advantages and related research of machine learning in TCM syndrome identification models.On this basis,this paper further analyzes the existing problems and proposes development strategies,such as the reasonable use of feature engineering to achieve accurate model construction,and the construction of integrated identification models with multimodal objective information sources,in order to provide references for future research on syndrome identification models.
杨倮;芦煜;王伟;马雪玲;刘红霞
浙江中医药大学,杭州 310053||北京中医药大学,北京 102401中国中医科学院中医药信息研究所,北京 100700北京中医药大学,北京 102401||证候与方剂基础广东省重点实验室(广州中医药大学),广州 510006北京中医药大学,北京 102401||证候与方剂基础研究北京市重点实验室(北京中医药大学),北京 102401||证候与方剂基础研究教育部重点实验室(北京中医药大学),北京 102401北京中医药大学,北京 102401
中医证候多模态机器学习辨识模型
Traditional Chinese medicineSyndromeMultimodal informationMachine learningIdentification model
《中华中医药杂志》 2026 (7)
1853-1859,7
国家自然科学基金区域创新发展联合基金项目重点项目(No.U24A20800),国家自然科学基金青年科学基金项目(No.81503382,No.82104739),教育部产学合作协同育人项目(No.220605634080722),中国博士后科学基金(No.2022M723531),中国中医科学院自由探索项目(No.ZZ160313),中国科学院重点部署项目(No.ZDRW-ZS2021-1-2),浙江中医药大学校级科研项目(No.2023RCZXZK28)
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