Analysis of medication regularity of traditional Chinese medicine prescriptions for gastropyretic excessiveness diabetes based on data mining / 中国中药杂志
China Journal of Chinese Materia Medica
; (24): 196-201, 2020.
Article
em Zh
| WPRIM
| ID: wpr-1008456
Biblioteca responsável:
WPRO
ABSTRACT
To analyze the medication regularity of traditional Chinese medicine(TCM) prescriptions for gastropyretic excessiveness diabetes recorded in Chinese Medicine Prescriptions Dictionary. A total of 103 eligible prescriptions were input into the system platform, and the Apriori algorithm was used to analyze their medication regularity. The 103 prescriptions for gastropyretic excessiveness diabetes were selected from the system, and 29 herb medicines were found with frequency of usage more than 8. Totally 33 commonly used herbal pairs(support degree≥10), twenty-three 3-herb core combinations(support degree≥8, confidence values≥0.5), and twenty-one 4-herb core combinations(confidence values≥0.5) were discovered after the medication regularity analysis by Apriori algorithm. The herbal medicine combinations with the highest correlation degree were discovered after the association rule analysis on the 103 prescriptions(support degree≥10, confidence values≥0.5). The four properties, five tastes, channel distributions and frequency of dose of the 103 prescriptions were also obtained after the corresponding analysis. According to the analysis and summary of the above data, the combination of Trichosanthis Radix, Anemarrhenae Rhizoma, Coptidis Rhizoma and Ophiopogonis Radix could reflect the medication regularity of TCM prescriptions for gastropyretic excessiveness diabetes to a certain degree, which is of great significance in guiding value in clinic.
Palavras-chave
Texto completo:
1
Índice:
WPRIM
Assunto principal:
Prescrições de Medicamentos
/
Medicamentos de Ervas Chinesas
/
Diabetes Mellitus
/
Mineração de Dados
/
Medicina Tradicional Chinesa
Limite:
Humans
Idioma:
Zh
Revista:
China Journal of Chinese Materia Medica
Ano de publicação:
2020
Tipo de documento:
Article