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.

ObjectiveTo achieve high-dimensional prediction of class imbalanced of adverse drug reaction（ADR） of traditional Chinese medicine（TCM） and to classify and identify risk factors affecting the occurrence of ADR based on the post-marketing safety data of TCM monitored centrally in real world hospitals. MethodThe ensemble clustering resampling combined with regularized Group Lasso regression was used to perform high-dimensional balancing of ADR class-imbalanced data， and then to integrate the balanced datasets to achieve ADR prediction and the risk factor identification by category. ResultA practical example study of the proposed method on a monitoring data of TCM injection performed that the accuracy of the ADR prediction， the prediction sensitivity， the prediction specificity and the area under receiver operating characteristic curve（AUC） were all above 0.8 on the test set. Meanwhile， 40 risk factors affecting the occurrence of ADR were screened out from total 600 high-dimensional variables. And the effect of risk factors on the occurrence of ADR was identified by classification weighting. The important risk factors were classified as follows：past history， medication information， name of combined drugs， disease status， number of combined drugs and personal data. ConclusionIn the real world data of rare ADR with a large amount of clinical variables， this paper realized accurate ADR prediction on high-dimensional and class imbalanced condition， and classified and identified the key risk factors and their clinical significance of categories， so as to provide risk early warning for clinical rational drug use and combined drug use， as well as scientific basis for reevaluation of safety of post-marketing TCM.

Objective To verify the reliability of novel 3D classification of intertrochanteric fractures by comparing the consistency between conventional and novel classifications.Methods Included for the present study were the preoperative X-ray and CT images of 189 patients with intertrochanteric fracture who had been hospitalized at Department of Orthopaedics,Beijing Chao Yang Hospital,Capital Medical University from 1 January,2017 to 1 January,2019.The patients' intertrochanteric fractures were classified by 6 orthopedic surgeons independently using Evans classification,Jensen classification,AO classification and novel 3D classification,respectively.One month later,the original images of the 189 patients were renumbered and classified again in the same way.The Kappa values between observers and within observers were calculated for the classifications of intertrochanteric fractures based on X-ray and CT images.Results In Evans classification,Jensen classification,AO classification and novel 3D classification,the interobserver Kappa values of X-ray films were 0.54 ± 0.03,0.53 ± 0.03,0.45 ± 0.03 and 0.63 ± 0.02,respectively,and the interobserver Kappa values of the CT images were 0.49 ± 0.03,0.49 ± 0.03,0.44 ± 0.04 and 0.63 ± 0.03.The intraobserver Kappa values of the X-ray films were 0.53 ± 0.02,0.54 ± 0.03,0.44 ±0.04 and 0.65 ± 0.02,respectively,and the intraobserver Kappa values of the CT images were 0.52 ± 0.03,0.52 ±0.03,0.41 ±0.02 and 0.64 ±0.03.In the novel classification based on X-ray and CT images,the interobserver and intraobserver Kappa values were both significantly higher than those in Evans,Jensen and AO classifications (P ＜ 0.05).Conclusion The novel 3D classification of intertrochanteric fractures is more reliable than the conventional ones.