摘要
疗效是中医存在和发展的根本,而中医药以个案、经验为证据的主要体现形式制约了中医药的传承与创新。如何融合现代临床流行病学与循证医学理念和方法,构建适合中医特点、符合国际规范的临床疗效评价方法是亟待解决的关键问题。针对此问题,通过梳理古代中医证据特征与内涵及其影响因素,探讨现代科研方法对产生高质量中医证据的促进与困境,明确建立真正符合中医本体特点的临床疗效评价方法是关键难点,提出在数据驱动的背景下,以真实世界研究、大数据、人工智能等为代表的新技术有望为创新中医临床疗效评价方法提供新思路。
Therapeutic effect is the foundation of the existence and development of traditional Chinese medicine(TCM). TCM efficacy is mainly reflected through cases and experiences, which has restricted the inheritance and innovation. Thus, how to integrate concepts and methods of modern clinical epidemiological and evidence-based medicine to construct clinical therapeutic effects evaluation method suitable for the characteristics of TCM and in line with international norms is a vital scientific problem to be solved urgently. For this problem, this paper sorts out the characteristics and connotation of ancient TCM evidence and its influencing factors, explores the promotion and dilemma of modern scientific research methods to produce high-quality TCM evidence, and clearly proposes that the establishment of the clinical therapeutic effects evaluation methods that truly conforms to the characteristics of TCM is an essential challenge. In the context of data-driven, new technologies represented by real-world study, big data, artificial intelligence are expected to provide ideas and methods for innovative TCM clinical therapeutic effects evaluation methods.
作者
符宇
邵明义
赵瑞霞
吴明慧
远佳瑶
余海滨
崔伟峰
FU Yu;SHAO Mingyi;ZHAO Ruixia;WU Minghui;YUAN Jiayao;YU Haibin;CUI Weifeng(The First Affiliated Hospital of Henan University of Chinese Medicine/Henan Evidence-based Medicine Center of Traditional Chinese Medicine,Zhengzhou,450000;Guangzhou University of Chinese Medicine;Henan University of Chinese Medicine;Henan Academy of Chinese Medicine)
出处
《中医杂志》
CSCD
北大核心
2020年第13期1124-1129,共6页
Journal of Traditional Chinese Medicine
基金
国家自然科学基金(81704011,81574101)
国家重点研发计划(2017YFC1703506)
河南省重点研发与推广专项(科技攻关)(192102310157)
河南省中医药科学研究专项(2017JDZX034,2018Y2061)。
关键词
临床疗效评价
循证医学
临床流行病学
真实世界研究
人工智能
clinical therapeutic effects evaluation
evidence-based medicine
clinical epidemiology
real-world study
artificial intelligence
