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Detection of Herb-Symptom Associations from Traditional Chinese Medicine Clinical Data
Joint Authors
Liu, Baoyan
Peng, Yonghong
Li, Yu-Bing
Zhou, Xue-Zhong
Wang, Ying-Hui
Hu, Jing-Qing
Xue, Yan-Xing
Xu, Li-Li
Xie, Qi
Liu, Xiao-fang
Zhang, Runshun
Source
Evidence-Based Complementary and Alternative Medicine
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-01-11
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Background.
Traditional Chinese medicine (TCM) is an individualized medicine by observing the symptoms and signs (symptoms in brief) of patients.
We aim to extract the meaningful herb-symptom relationships from large scale TCM clinical data.
Methods.
To investigate the correlations between symptoms and herbs held for patients, we use four clinical data sets collected from TCM outpatient clinical settings and calculate the similarities between patient pairs in terms of the herb constituents of their prescriptions and their manifesting symptoms by cosine measure.
To address the large-scale multiple testing problems for the detection of herb-symptom associations and the dependence between herbs involving similar efficacies, we propose a network-based correlation analysis (NetCorrA) method to detect the herb-symptom associations.
Results.
The results show that there are strong positive correlations between symptom similarity and herb similarity, which indicates that herb-symptom correspondence is a clinical principle adhered to by most TCM physicians.
Furthermore, the NetCorrA method obtains meaningful herb-symptom associations and performs better than the chi-square correlation method by filtering the false positive associations.
Conclusions.
Symptoms play significant roles for the prescriptions of herb treatment.
The herb-symptom correspondence principle indicates that clinical phenotypic targets (i.e., symptoms) of herbs exist and would be valuable for further investigations.
American Psychological Association (APA)
Li, Yu-Bing& Zhou, Xue-Zhong& Zhang, Runshun& Wang, Ying-Hui& Peng, Yonghong& Hu, Jing-Qing…[et al.]. 2015. Detection of Herb-Symptom Associations from Traditional Chinese Medicine Clinical Data. Evidence-Based Complementary and Alternative Medicine،Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1061298
Modern Language Association (MLA)
Li, Yu-Bing…[et al.]. Detection of Herb-Symptom Associations from Traditional Chinese Medicine Clinical Data. Evidence-Based Complementary and Alternative Medicine No. 2015 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1061298
American Medical Association (AMA)
Li, Yu-Bing& Zhou, Xue-Zhong& Zhang, Runshun& Wang, Ying-Hui& Peng, Yonghong& Hu, Jing-Qing…[et al.]. Detection of Herb-Symptom Associations from Traditional Chinese Medicine Clinical Data. Evidence-Based Complementary and Alternative Medicine. 2015. Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1061298
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1061298