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The Identification of Chinese Herbal Medicine Combination Association Rule Analysis Based on an Improved Apriori Algorithm in Treating Patients with COVID-19 Disease.
Zheng, Yanyan; Chen, Ying.
  • Zheng Y; Department of Computer Science, Taizhou University, Taizhou 318000, China.
  • Chen Y; Department of Computer Science, Taizhou University, Taizhou 318000, China.
J Healthc Eng ; 2022: 6337082, 2022.
Article in English | MEDLINE | ID: covidwho-2020520
ABSTRACT
In this work, an improved Apriori algorithm is proposed. The main goal is to improve the processing efficiency of the algorithm, and the idea and process of the Apriori algorithm are optimized. The proposed method is compared with the classical association rule algorithm to verify its effectiveness. Traditional Chinese medicine plays a certain role in the prevention and treatment of COVID-19. In order to deeply mine the association rules between Chinese herbal medicines for the prevention and treatment of COVID-19, this improved Apriori algorithm is applied from the retrieved published scientific literature and the guidelines for the prevention and treatment of COVID-19 published all over China. Based on the representation of traditional Chinese medicine data in binary form, the potential core traditional Chinese medicine combinations in the treatment of COVID-19 are identified. The results of association rules of Chinese herbal medicine data obtained from the real database provide an important reference for the analysis of COVID-19 combined treatment of Chinese herbal medicine.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Drugs, Chinese Herbal / COVID-19 Drug Treatment Topics: Traditional medicine Limits: Humans Country/Region as subject: Asia Language: English Journal: J Healthc Eng Year: 2022 Document Type: Article Affiliation country: 2022

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Drugs, Chinese Herbal / COVID-19 Drug Treatment Topics: Traditional medicine Limits: Humans Country/Region as subject: Asia Language: English Journal: J Healthc Eng Year: 2022 Document Type: Article Affiliation country: 2022