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Exploring medication rules in Chinese randomized controlled trials of type 2 diabetes based on data mining technology.

Authors :
Zhi-Li Dou
Hao-Nan Sun
Yu-Nan Zhang
Lei Zhao
Zhe Huang
Shu-Jing Xu
Yi-Xing Liu
Dong-Ran Han
Jin-Zhu Jia
Source :
Medical Data Mining. 2023, Vol. 6 Issue 4, p1-12. 12p.
Publication Year :
2023

Abstract

Background: To systematically summarize and categorize the Chinese herbal medicine in the domestic traditional Chinese medicine (TCM) literature on type 2 diabetes mellitus (T2DM), in this paper, we mine traditional Chinese medicine data for relationships and provide for future practitioners and researchers. Methods: Taking randomized controlled trials on the treatment of T2DM in TCM as the research theme, we searched for full-text literature in three major clinical databases, including CNKI, Wan Fang, and VIP, published between 1990 and 2020. We then conducted frequency statistics, cluster analysis, association rules extraction, and principal component analysis based on a corpus of medical academic words extracted from 1116 research articles. Results: The most frequently used is Astragali Radix, and the most commonly used two-herb combination in T2DM treatment consisted of Coptidis Rhizoma and Moutan Cortex. Moutan Cortex, Alismatis Rhizoma, and Dioscoreae Rhizoma were the most frequently used three-herb combination. We found a "lung" and "liver" and "kidney" model and confirmed the value of classical meridian tropism theory and pattern identification. The treatment is mainly to fill deficiency and clear heat and consider water infiltration, dampness, blood circulation, and silt. Conclusion: This study provides an in-depth perspective on the TCM medication rules for T2DM and offers practitioners and researchers valuable information about the current status and frontier trends of TCM research on T2DM in terms of diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
26241587
Volume :
6
Issue :
4
Database :
Academic Search Index
Journal :
Medical Data Mining
Publication Type :
Academic Journal
Accession number :
176063482
Full Text :
https://doi.org/10.53388/MDM202306025