A study of TCM master Yan Zhenghua's medication rule in prescriptions for digestive system diseases based on Apriori and complex system entropy cluster

Objective: To explore Yan Zhenghua's drug selection rule for treating digestive system diseases using data mining. Methods: The 609 medical records of digestive system diseases treated by Yan Zhenghua were collected and the herbs in these recipes were examined using a data mining technique. The...

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Main Authors: Jiarui Wu, Weixian Guo, Yan Tang, Aiqing Han, Bing Yang, Dan Zhang, Bing Zhang
Format: Article
Language:English
Published: Elsevier 2015-10-01
Series:Journal of Traditional Chinese Medical Sciences
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2095754816000326
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spelling doaj-7dd075a1339c4aaa80562b43bb05217f2021-04-02T01:35:42ZengElsevierJournal of Traditional Chinese Medical Sciences2095-75482015-10-012424124710.1016/j.jtcms.2016.02.007A study of TCM master Yan Zhenghua's medication rule in prescriptions for digestive system diseases based on Apriori and complex system entropy clusterJiarui Wu0Weixian Guo1Yan Tang2Aiqing Han3Bing Yang4Dan Zhang5Bing Zhang6Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100102, ChinaDepartment of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100102, ChinaInformation Center, Beijing University of Chinese Medicine, Beijing 100029, ChinaInformation Center, Beijing University of Chinese Medicine, Beijing 100029, ChinaDepartment of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100102, ChinaDepartment of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100102, ChinaDepartment of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100102, ChinaObjective: To explore Yan Zhenghua's drug selection rule for treating digestive system diseases using data mining. Methods: The 609 medical records of digestive system diseases treated by Yan Zhenghua were collected and the herbs in these recipes were examined using a data mining technique. The correlativity between herb pairs and association rules was studied using an Apriori algorithm and the correlativity among multi-herbs was studied using a complex system entropy cluster technique. Results: Yan Zhenghua's treatment of digestive system diseases featured 15 herbs prescribed at least 159 times each, 22 herb pairs prescribed at least 155 times each, and eight frequently used herb core combinations. A confidence greater than 0.91 and a support level greater than 20% were achieved using the modified mutual information method. Conclusion: The data mining results conformed to findings from clinical practice. The data mining method is a valuable technique with which to study the experience of famous, elderly traditional Chinese medicine physicians.http://www.sciencedirect.com/science/article/pii/S2095754816000326Yan ZhenghuaComplex system entropy clusterDigestive system diseasesMedication rule
collection DOAJ
language English
format Article
sources DOAJ
author Jiarui Wu
Weixian Guo
Yan Tang
Aiqing Han
Bing Yang
Dan Zhang
Bing Zhang
spellingShingle Jiarui Wu
Weixian Guo
Yan Tang
Aiqing Han
Bing Yang
Dan Zhang
Bing Zhang
A study of TCM master Yan Zhenghua's medication rule in prescriptions for digestive system diseases based on Apriori and complex system entropy cluster
Journal of Traditional Chinese Medical Sciences
Yan Zhenghua
Complex system entropy cluster
Digestive system diseases
Medication rule
author_facet Jiarui Wu
Weixian Guo
Yan Tang
Aiqing Han
Bing Yang
Dan Zhang
Bing Zhang
author_sort Jiarui Wu
title A study of TCM master Yan Zhenghua's medication rule in prescriptions for digestive system diseases based on Apriori and complex system entropy cluster
title_short A study of TCM master Yan Zhenghua's medication rule in prescriptions for digestive system diseases based on Apriori and complex system entropy cluster
title_full A study of TCM master Yan Zhenghua's medication rule in prescriptions for digestive system diseases based on Apriori and complex system entropy cluster
title_fullStr A study of TCM master Yan Zhenghua's medication rule in prescriptions for digestive system diseases based on Apriori and complex system entropy cluster
title_full_unstemmed A study of TCM master Yan Zhenghua's medication rule in prescriptions for digestive system diseases based on Apriori and complex system entropy cluster
title_sort study of tcm master yan zhenghua's medication rule in prescriptions for digestive system diseases based on apriori and complex system entropy cluster
publisher Elsevier
series Journal of Traditional Chinese Medical Sciences
issn 2095-7548
publishDate 2015-10-01
description Objective: To explore Yan Zhenghua's drug selection rule for treating digestive system diseases using data mining. Methods: The 609 medical records of digestive system diseases treated by Yan Zhenghua were collected and the herbs in these recipes were examined using a data mining technique. The correlativity between herb pairs and association rules was studied using an Apriori algorithm and the correlativity among multi-herbs was studied using a complex system entropy cluster technique. Results: Yan Zhenghua's treatment of digestive system diseases featured 15 herbs prescribed at least 159 times each, 22 herb pairs prescribed at least 155 times each, and eight frequently used herb core combinations. A confidence greater than 0.91 and a support level greater than 20% were achieved using the modified mutual information method. Conclusion: The data mining results conformed to findings from clinical practice. The data mining method is a valuable technique with which to study the experience of famous, elderly traditional Chinese medicine physicians.
topic Yan Zhenghua
Complex system entropy cluster
Digestive system diseases
Medication rule
url http://www.sciencedirect.com/science/article/pii/S2095754816000326
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