Analysis of medication rules of Chinese patent medicine in treating diabetes recorded in the three catalogs based on data mining

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Author:  SU Fenli ¹ LIU Chaonan ² YAO Yuan ¹ FENG Shiyin ¹ YAN Xue ¹ QIU Zhenwen ¹

Affiliation: 1. Department of Pharmacy, First Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Guangzhou 510405, China 2. Department of Endocrine, First Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Guangzhou 510405, China

Keywords: Diabetes Chinese patent medicine Medication rule Data mining

DOI: 10.12173/j.issn.2097-4922.202501015

Reference: SU Fenli, LIU Chaonan, YAO Yuan, FENG Shiyin, YAN Xue, QIU Zhenwen. Analysis of medication rules of Chinese patent medicine in treating diabetes recorded in the three catalogs based on data mining[J]. Yaoxue QianYan Zazhi, 2025, 29(4): 642-649. DOI: 10.12173/j.issn.2097-4922.202501015.[Article in Chinese]  Copied

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Abstract

Objective Data mining methods were used to analyze the medication rules of Chinese patent medicine in treating diabetes.

Methods Chinese patent medicines in Chinese Pharmacopeia (2020 edition), National Essential Drugs List (2018 edition) and Medicine List for National Basic Medical Insurance, Employment Injury Insurance and Maternity Insurance (2024 edition) were collected. Descriptive statistical analysis was performed by using Microsoft Office Excel 2019 software. SPSS Modeler 18.0 software was used to analyze the association rules of prescription drugs. Cluster analysis was conducted to investigate the Chinese medicines used 6 or more times for diabetes by using SPSS 22.0.

Results 32 kinds of Chinese patent medicines were included, involving 81 traditional Chinese medicines, mainly for tonifying deficiency, clearing heat and promoting blood circulation. The medicinal properties were mainly cold, followed by cool and warm, and the medicinal flavor was mainly sweet, followed by bitter and tangy. The main meridian distribution belonged to the lung, kidney and liver. 11 groups of strongly associated drug pairs were obtained by association rule analysis, among which Trichosanthis Radix →Astragali Radix had the highest frequency, and Ginseng Radix Et Rhizoma+Schisandrae Chinensis Fructus→ Astragali Radix had the highest confidence. High-correlation traditional Chinese medicines were primarily based on modifications of Shengmai Powder combined with Yuye Decoction.

Conclusion Data mining technology can reveal the hidden formula formation rules of Chinese patent medicines for treating diabetes, and provide certain reference for clinical drug compatibility and new drug development of hypoglycemic Chinese patent medicines.

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References

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