针灸和草药（英文）2026，Vol.6Issue(2)：141-154,14.DOI:10.1097/HM9.0000000000000198
针灸研究中的多模态与数据驱动方法:方法、应用与挑战
Multimodal and data-driven approaches in acupuncture research:methods,applications,and challenges
摘要
Abstract
Acupuncture research increasingly involves heterogeneous and multimodal data that are difficult to analyze using conventional methods.This review summarizes data-driven approaches in acupuncture research within a framework encompassing intervention,response,and contextual data.We discuss causal inference,artificial intelligence,text mining,and integrative analysis,along with their applications in efficacy evaluation,outcome prediction,mechanistic investigation,and clinical decision support.These approaches shift the focus of acupuncture research from population-level average effects toward individualized clinical decision-making by enabling the analysis of treatment heterogeneity and underlying mechanisms.However,current research remains limited by inadequate data standardization,insufficient external validation,and limited model interpretability.Despite these challenges,data-driven approaches offer substantial promise for advancing more rigorous and personalized acupuncture research.关键词
针灸/数据驱动/因果推断/机器学习/深度学习/多模态数据/临床决策支持/机制研究Key words
Acupuncture/Causal inference/Clinical decision support/Data-driven/Deep learning/Machine learning/Mechanistic studies/Multimodal data引用本文复制引用
聂德慧,黄建龙,姚高磊,陆丽明,荣培晶,黄璞琛,金丹,陈一鸣,范宝超,黄闪闪,张誉清,周钰,韩斌..针灸研究中的多模态与数据驱动方法:方法、应用与挑战[J].针灸和草药（英文）,2026,6(2):141-154,14.基金项目
This study was supported by the Zhongshan TCM Heritage and Innovation Research Program(No.2024B3006),the Peak-Shaping Project under Guangzhou University of Chinese Medicine's Action Plan for Double First-Class and High-Level Disciplinary Development(No.GZY2025ZJ18),the Sanming Project of Medicine in Shenzhen(No.SZZYSM202311015),and the Shenzhen Medical Research Fund(No.C2501027). （No.2024B3006）
