Analysis of medication characteristics of traditional Chinese medicine in treating coronavirus disease-19 based on data mining

To analysis the medication characteristics of the prescriptions issued via open channel by the National and Provincial Health Committee and the State Administration of Traditional Chinese Medicine in treating coronavirus disease 2019 (COVID-19). We collected the data of traditional Chinese medicine...

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Bibliographic Details
Published in:Zhejiang da xue xue bao. Journal of Zhejiang University. Medical sciences. Yi xue ban 2020-05, Vol.49 (1), p.260
Main Authors: Fan, Tiantian, Chen, Yongcan, Bai, Yu, Ma, Fengqi, Wang, Hengcang, Yang, Yiping, Chen, Jinxu, Lin, Yuqi
Format: Article
Language:Chinese
Subjects:
Betacoronavirus
Cluster Analysis
Coronavirus Infections - therapy
COVID-19
Data Mining
Drugs, Chinese Herbal - analysis
Drugs, Chinese Herbal - therapeutic use
Humans
Medicine, Chinese Traditional - statistics & numerical data
Pandemics
Pneumonia, Viral - therapy
SARS-CoV-2
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Summary:To analysis the medication characteristics of the prescriptions issued via open channel by the National and Provincial Health Committee and the State Administration of Traditional Chinese Medicine in treating coronavirus disease 2019 (COVID-19). We collected the data of traditional Chinese medicine related to treatment plans published by the National and Provincial Health Committee and the State Administration of Traditional Chinese Medicine from the start of COVID-19 outbreak in Wuhan to February 19, 2020. The frequency analysis, cluster analysis and correlation analysis were performed. The study collected 4 national and 34 regional prevention and treatment plans, 578 items, 84 traditional Chinese formulations, 60 Chinese patent medicines, and 230 Chinese herbs. The high frequently used herbs were , , , and . The commonly used traditional formulations included Decoction, Powder, and Decoction. The Chinese patent drugs included Pill, Injection, and Capsule. The most common paired medications were and , and .
ISSN:1008-9292