机器学习与中医的融合。

The integration of machine learning into traditional Chinese medicine.

作者信息

Hong Yanfeng, Zhu Sisi, Liu Yuhong, Tian Chao, Xu Hongquan, Chen Gongxing, Tao Lin, Xie Tian

机构信息

School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.

Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou, 311121, China.

出版信息

J Pharm Anal. 2025 Aug;15(8):101157. doi: 10.1016/j.jpha.2024.101157. Epub 2024 Dec 4.

DOI:10.1016/j.jpha.2024.101157

PMID:40823261

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12356308/

Abstract

Traditional Chinese medicine (TCM) is an ancient medical system distinctive and effective in treating cancer, depression, coronavirus disease 2019 (COVID-19), and other diseases. However, the relatively abstract diagnostic methods of TCM lack objective measurement, and the complex mechanisms of action are difficult to comprehend, which hinders the application and internationalization of TCM. Recently, while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research, the rise of machine learning (ML) has significantly enhanced their integration with TCM. This article introduces representative methodological cases in quality control, mechanism research, diagnosis, and treatment processes of TCM, revealing the potential applications of ML technology in TCM. Furthermore, the challenges faced by ML in TCM applications are summarized, and future directions are discussed.

摘要

中医是一种古老的医学体系，在治疗癌症、抑郁症、2019冠状病毒病（COVID-19）等疾病方面独具特色且疗效显著。然而，中医相对抽象的诊断方法缺乏客观测量，其复杂的作用机制难以理解，这阻碍了中医的应用和国际化。近年来，虽然在利用网络药理学和虚拟筛选等方法进行中医研究方面取得了突破，但机器学习（ML）的兴起显著增强了它们与中医的融合。本文介绍了中医质量控制、机制研究、诊断和治疗过程中的代表性方法案例，揭示了ML技术在中医中的潜在应用。此外，总结了ML在中医应用中面临的挑战，并探讨了未来的发展方向。