首页|期刊导航|针灸和草药(英文)|针灸研究中的多模态与数据驱动方法:方法、应用与挑战

针灸研究中的多模态与数据驱动方法:方法、应用与挑战OA

Multimodal and data-driven approaches in acupuncture research:methods,applications,and challenges

中文摘要英文摘要

随着针灸研究的不断深入,研究数据呈现出异质性强、来源多样和多模态融合等特点,传统分析方法在处理此类复杂数据时面临一定局限.文章以针灸研究中的干预数据、疗效反应数据和背景情境数据为基本框架,系统梳理数据驱动方法在针灸研究中的应用进展,重点介绍因果推断、人工智能、文本挖掘及多源数据整合分析等方法,并探讨其在疗效评价、结局预测、作用机制阐释和临床决策支持中的应用价值.数据驱动方法有助于揭示针灸疗效的个体差异和潜在作用机制,推动针灸研究由关注群体平均效应逐步转向支持个体化临床决策.然而,当前相关研究仍存在数据标准化不足、外部验证不充分和模型可解释性有限等问题.未来,随着高质量多模态数据的积累和分析方法的不断完善,数据驱动方法有望为开展更加严谨、精准和个体化的针灸研究提供重要支撑.

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.

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

广州中医药大学针灸推拿康复临床医学院,广州广州中医药大学附属中山中医院(中山市中医院),中山广州中医药大学针灸推拿康复临床医学院,广州广州中医药大学针灸推拿康复临床医学院,华南针灸研究中心临床研究与大数据实验室,广州中国中医科学院临床医学基础研究所,北京广州中医药大学针灸推拿康复临床医学院,广州广州中医药大学附属中山中医院(中山市中医院),中山广州中医药大学针灸推拿康复临床医学院,华南针灸研究中心临床研究与大数据实验室,广州广州中医药大学针灸推拿康复临床医学院,广州广州中医药大学针灸推拿康复临床医学院,广州麦克马斯特大学健康研究方法、证据与影响系,汉密尔顿||中国中医科学院广安门医院循证整合医学中心(CEBIM)-Clarity 合作组,北京深圳宝安纯中医治疗医院针灸科,深圳广州中医药大学附属中山中医院(中山市中医院),中山

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

AcupunctureCausal inferenceClinical decision supportData-drivenDeep learningMachine learningMechanistic studiesMultimodal data

《针灸和草药(英文)》 2026 (2)

141-154,14

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).

10.1097/HM9.0000000000000198

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