【摘要】 中医药真实世界临床评价（RWCE-TCM）是一种综合评价中医药临床效果的前沿方法，通过深度挖掘真实世界数据（RWD），全面展现中医药干预的实际疗效与个体化诊疗优势，旨在探讨其干预与临床结果之间的因果关系，为人用经验提供科学证据支持。大规模RWD多为观察性数据，面对其复杂性、高维度、类不平衡及病证多样性等挑战，传统相关性分析已显不足，亟须引入因果推断以增强分析的准确性，这对中医药的临床定位、优势人群、特色疗法等分析至关重要。该文创新性地提出了十步数据科学方法论，针对性地解决RWCE-TCM中的数据难题。该方法论涵盖了数据采集融合、高维特征选择、可解释统计机器学习、复杂图网络分析、自然语言处理、因果学习及智能引擎构建等先进技术，采用自底向上策略，从RWD中提炼临床问题，明确研究目标。通过适时灵活的数据收集与观察性研究，不断迭代分析过程，构建精准的统计因果模型，并据此设计各类随机对照试验以验证因果关系。此方法论不仅可促进中医药RWD的智能转化与科学论证，还可深度挖掘可解释的诊疗规律，引领数据驱动的因果转化分析，提升RWCE-TCM的效能和可信度，为中医药临床应用的科学指导开辟了新的路径。更多还原

【Abstract】 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 chal enges 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.更多还原