scholarly journals Real world study on treatment of CKD by traditional Chinese medicine Kidney Flaccidity Compound

2021 ◽  
Vol 48 ◽  
pp. 102070
Author(s):  
Hongxi Chen ◽  
Nan Mao ◽  
Xin Ma ◽  
Junming Fan
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Real World

scholarly journals Real-world evidence study for distribution of traditional Chinese medicine syndrome and its elements on chronic bronchitis in China

2019 ◽  
Vol 02 (04) ◽  
pp. 155-163
Author(s):  
Qing Kong ◽  
Mihui Li ◽  
Xuanfeng Qin ◽  
Yubao Lv ◽  
Zihui Tang
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Chronic Bronchitis ◽  
Real World ◽  
Association Rule ◽  
Factor Analyses ◽  
Real World Data ◽  
Cluster Analyses ◽  
The Core ◽  
Tcm Syndrome

Objective: To investigate the distribution and characteristics of traditional Chinese medicine (TCM) syndromes and its elements on chronic bronchitis (CB) based on real-world data (RWD) so as to optimize the treatment strategies. Methods: A real-world study based on 2207 medical records collected from five hospitals in China, to explore the relationship between TCM syndrome and CB using the big data methods. Factor analyses were used to reduce the dimensions of TCM syndrome elements and found common factors. Additionally, cluster analyses were performed to value combinations of TCM syndrome element. Finally, association rule analyses were employed to assess the structures of TCM syndromes elements and estimate the patterns of TCM syndrome. Results: A total of 21 TCM syndromes were extracted from RWD in this work. There were four TCM syndromes consisting of Tan_Zhuo_Zu_Fei, Tan_Re_Yong_Fei, Feng_Han_Xi_Fei, and Feng_Re_Fan_Fei with [Formula: see text]% frequency based on the distribution frequency. The two top Xu TCM syndromes of Fei_Yin_Xu and Fei_Shen_Qi_Xu were identified. The top six pathogenesis TCM syndrome elements were Tan, Huo, Feng, Han, Qi_Xu, and Yin_Xu. Factor analyses, cluster analyses, and association rule analyses demonstrated that Tan, Huo, Feng, Han, Qi-Xu, Yin-Xu, Fei, and Shen were the core TCM syndrome elements. Conclusion: The four common Shi TCM syndromes of Tan_Zhuo_Zu_Fei, Tan_Re_Yong_Fei, Feng_Han_Xi_Fei, and Feng_Re_Fan_Fei for CB were detected in the real world study, and the two Xu TCM syndromes of Fei_Yin_Xu and Fei_Shen_Qi_Xu were identified. The Mix TCM syndrome of Fei_Pi_Qi_Xu_Tan_Shi_Yun_Fei was the main syndrome. The core TCM syndrome elements of Tan, Huo, Feng, Han, Qi_Xu, and Yin_Xu, Fei, and Shen were determined in the entire sample.

The Chang Gung Research Database: Multi‐Institutional Real‐World Data Source for Traditional Chinese Medicine in Taiwan

2021 ◽  
Author(s):  
Shih‐Chieh Shao ◽  
Edward Chia‐Cheng Lai ◽  
Tse‐Hung Huang ◽  
Ming‐Jui Hung ◽  
Ming‐Shao Tsai ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Real World ◽  
Research Database ◽  
Real World Data ◽  
World Data ◽  
Data Source

scholarly journals Discovering Regularities from Traditional Chinese Medicine Prescriptions via Bipartite Embedding Model

2019 ◽  
Author(s):  
Chunyang Ruan ◽  
Jiangang Ma ◽  
Ye Wang ◽  
Yanchun Zhang ◽  
Yun Yang
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clinical Experience ◽  
Real World ◽  
Heterogeneous Network ◽  
Specific Problem ◽  
Graph Embedding ◽  
Clinical Quality ◽  
Real World Datasets ◽  
Relation Prediction

Regularities analysis for prescriptions is a significant task for traditional Chinese medicine (TCM), both in inheritance of clinical experience and in improvement of clinical quality. Recently, many methods have been proposed for regularities discovery, but this task is challenging due to the quantity, sparsity and free-style of prescriptions. In this paper, we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions. We model this task as a relation prediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relationships. Specifically, we first establish a heterogeneous network with herbs and symptoms as its nodes. We develop a bipartite embedding model termed HS2Vec to detect regularities, which explores multiple relations of herbherb, and herb-symptom based on the heterogeneous network. Experiments on four real-world datasets demonstrate that the proposed framework is very effective for regularities discovery.

scholarly journals Acceptability of Traditional Chinese Medicine in Chinese People Based on 10-Year's Real World Study With Mutiple Big Data Mining

2022 ◽  
Vol 9 ◽  
Author(s):  
Yan Guo ◽  
Tengjiao Wang ◽  
Wei Chen ◽  
Ted J. Kaptchuk ◽  
Xilian Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Real World ◽  
Large Scale ◽  
Point Of View ◽  
Highly Educated ◽  
The Past ◽  
Medical Visits

In the past decades, numerous clinical researches have been conducted to illuminate the effects of traditional Chinese medicine for better inheritance and promotion of it, which are mostly clinical trials designed from the doctor's point of view. This large-scale data mining study was conducted from real-world point of view in up to 10 years' big data sets of Traditional Chinese Medicine (TCM) in China, including both medical visits to hospital and cyberspace and contemporaneous social survey data. Finally, some important and interesting findings appear: (1) More Criticisms vs. More Visits. The intensity of criticism increased by 2.33 times over the past 10 years, while the actual number of visits increased by 2.41 times. (2) The people of younger age, highly educated and from economically developed areas have become the primary population for utilizing TCM, which is contrary to common opinions on the characteristics of TCM users. The discovery of this phenomenon indicates that TCM deserves further study on how it treats illness and maintains health.

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