Suxian Zhang1, Hao Wu1, Jie Liu2, Huihui Gu1, Xiujuan Li1, Tiansong Zhang1,3. 1. Department of Traditional Chinese Medicine (TCM), Jing'an District Central Hospital, Fudan University, Shanghai 200040, China. 2. Shandong University of Traditional Chinese Medicine, Jinan 250014, China. 3. Institute of Standardization, Institutes of Integrative Medicine, Fudan University, Shanghai 200040, China.
Abstract
BACKGROUND: Treatment of pulmonary fibrosis by traditional Chinese medicine (TCM) has accumulated important experience. Our interest is in exploring the medication regularity of contemporary Chinese medical specialists treating pulmonary fibrosis. METHODS: Through literature search, medical records from TCM experts who treat pulmonary fibrosis, which were published in Chinese and English medical journals, were selected for this study. As the object of study, a database was established after analysing the records. After data cleaning, the rules of medicine in the treatment of pulmonary fibrosis in medical records of TCM were explored by using data mining technologies such as frequency analysis, association rule analysis, and link analysis. RESULTS: A total of 124 medical records from 60 doctors were selected in this study; 263 types of medicinals were used a total of 5,455 times; the herbs that were used more than 30 times can be grouped into 53 species and were used a total of 3,681 times. Using main medicinals cluster analysis, medicinals were divided into qi-tonifying, yin-tonifying, blood-activating, phlegm-resolving, cough-suppressing, panting-calming, and ten other major medicinal categories. According to the set conditions, a total of 62 drug compatibility rules have been obtained, involving mainly qi-tonifying, yin-tonifying, blood-activating, phlegm-resolving, qi-descending, and panting-calming medicinals, as well as other medicinals used in combination. CONCLUSIONS: The results of data mining are consistent with clinical practice and it is feasible to explore the medical rules applicable to the treatment of pulmonary fibrosis in medical records of TCM by data mining.
BACKGROUND: Treatment of pulmonary fibrosis by traditional Chinese medicine (TCM) has accumulated important experience. Our interest is in exploring the medication regularity of contemporary Chinese medical specialists treating pulmonary fibrosis. METHODS: Through literature search, medical records from TCM experts who treat pulmonary fibrosis, which were published in Chinese and English medical journals, were selected for this study. As the object of study, a database was established after analysing the records. After data cleaning, the rules of medicine in the treatment of pulmonary fibrosis in medical records of TCM were explored by using data mining technologies such as frequency analysis, association rule analysis, and link analysis. RESULTS: A total of 124 medical records from 60 doctors were selected in this study; 263 types of medicinals were used a total of 5,455 times; the herbs that were used more than 30 times can be grouped into 53 species and were used a total of 3,681 times. Using main medicinals cluster analysis, medicinals were divided into qi-tonifying, yin-tonifying, blood-activating, phlegm-resolving, cough-suppressing, panting-calming, and ten other major medicinal categories. According to the set conditions, a total of 62 drug compatibility rules have been obtained, involving mainly qi-tonifying, yin-tonifying, blood-activating, phlegm-resolving, qi-descending, and panting-calming medicinals, as well as other medicinals used in combination. CONCLUSIONS: The results of data mining are consistent with clinical practice and it is feasible to explore the medical rules applicable to the treatment of pulmonary fibrosis in medical records of TCM by data mining.
Entities:
Keywords:
Pulmonary fibrosis; data mining; medical records of traditional Chinese medicine (TCM); medication regularity
Authors: Ganesh Raghu; Bram Rochwerg; Yuan Zhang; Carlos A Cuello Garcia; Arata Azuma; Juergen Behr; Jan L Brozek; Harold R Collard; William Cunningham; Sakae Homma; Takeshi Johkoh; Fernando J Martinez; Jeffrey Myers; Shandra L Protzko; Luca Richeldi; David Rind; Moisés Selman; Arthur Theodore; Athol U Wells; Henk Hoogsteden; Holger J Schünemann Journal: Am J Respir Crit Care Med Date: 2015-07-15 Impact factor: 21.405
Authors: Ganesh Raghu; Harold R Collard; Jim J Egan; Fernando J Martinez; Juergen Behr; Kevin K Brown; Thomas V Colby; Jean-François Cordier; Kevin R Flaherty; Joseph A Lasky; David A Lynch; Jay H Ryu; Jeffrey J Swigris; Athol U Wells; Julio Ancochea; Demosthenes Bouros; Carlos Carvalho; Ulrich Costabel; Masahito Ebina; David M Hansell; Takeshi Johkoh; Dong Soon Kim; Talmadge E King; Yasuhiro Kondoh; Jeffrey Myers; Nestor L Müller; Andrew G Nicholson; Luca Richeldi; Moisés Selman; Rosalind F Dudden; Barbara S Griss; Shandra L Protzko; Holger J Schünemann Journal: Am J Respir Crit Care Med Date: 2011-03-15 Impact factor: 21.405