Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and cl...Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical outcomes.The study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for RWCE-TCM.This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM.The methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational analysis.The goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic therapies.In addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical data.We constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized treatment.The causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical evidence.Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM.The proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of TCM.This method supports the intelligent translation and demonstration of RWD in TCM,leads the data-driven translational analysis of causal learning,and innovates the path of RWCE-TCM.展开更多
[目的/意义]依据"信息"和"情报"的语义内涵不同,厘清我国"图书馆学情报学"(library and information science)与情报学(intelligence studies)的学科内涵差异,进而为划清学科边界提供依据,结束图书馆信...[目的/意义]依据"信息"和"情报"的语义内涵不同,厘清我国"图书馆学情报学"(library and information science)与情报学(intelligence studies)的学科内涵差异,进而为划清学科边界提供依据,结束图书馆信息学的迷惘和"情报学"乱象,有利于各自学科的科学建设与健康发展。[方法/过程]从语义学视角切入,通过对"图书馆学情报学"与情报学两个"舶来品"学科的追根溯源,运用中外现实教育中的课程设置对比,探寻两个学科依迥异方法论准则和程序建立起的规则,推导并证明其不同的学科属性。[结果/结论]"信息"与"情报"语义内涵不同决定了"图书馆学情报学"与情报学的本质差异,我国的"图书馆学情报学"实际该称之为"图书馆信息学",与情报学无关。依方法论准则和程序建立起的规则迥异决定了"图书馆情报学"与情报学不同的学科属性,我国学科设置中的"情报学"也实则"信息学"。对"图书馆学情报学"进行改革的精神可嘉,但不可强行融合或将"情报学"从原有学科体系中拉出另起炉灶,否则极易造成图书馆信息学的空心化,也不利于情报学的健康发展,故应依据不同学科内涵相互借鉴和适度交叉,走各自建设之路是有利于各自学科建设和发展的应有理性选择。展开更多
基金This work was funded by the scientific and technological innovation project of China Academy of Chinese Medical Sciences(CI2021A04706,CI2021B003)the National Key Research and Development Program of China(2023YFC3503404,2017YFC1700406-2,2018YFC1704306)the independent selection project of China Academy of Chinese Medical Sciences(Z0643,Z0723).
文摘Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical outcomes.The study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for RWCE-TCM.This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM.The methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational analysis.The goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic therapies.In addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical data.We constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized treatment.The causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical evidence.Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM.The proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of TCM.This method supports the intelligent translation and demonstration of RWD in TCM,leads the data-driven translational analysis of causal learning,and innovates the path of RWCE-TCM.
文摘[目的/意义]依据"信息"和"情报"的语义内涵不同,厘清我国"图书馆学情报学"(library and information science)与情报学(intelligence studies)的学科内涵差异,进而为划清学科边界提供依据,结束图书馆信息学的迷惘和"情报学"乱象,有利于各自学科的科学建设与健康发展。[方法/过程]从语义学视角切入,通过对"图书馆学情报学"与情报学两个"舶来品"学科的追根溯源,运用中外现实教育中的课程设置对比,探寻两个学科依迥异方法论准则和程序建立起的规则,推导并证明其不同的学科属性。[结果/结论]"信息"与"情报"语义内涵不同决定了"图书馆学情报学"与情报学的本质差异,我国的"图书馆学情报学"实际该称之为"图书馆信息学",与情报学无关。依方法论准则和程序建立起的规则迥异决定了"图书馆情报学"与情报学不同的学科属性,我国学科设置中的"情报学"也实则"信息学"。对"图书馆学情报学"进行改革的精神可嘉,但不可强行融合或将"情报学"从原有学科体系中拉出另起炉灶,否则极易造成图书馆信息学的空心化,也不利于情报学的健康发展,故应依据不同学科内涵相互借鉴和适度交叉,走各自建设之路是有利于各自学科建设和发展的应有理性选择。
