title: Detection of herb-symptom associations from traditional chinese medicine clinical data.
abstract: 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.
authors: School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China.
authors: Yu-Bing
authors: YB
authors: Li
authors: School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China.
authors: Xue-Zhong
authors: XZ
authors: Zhou
authors: Guanganmen Hospital, China Academy of Chinese Medicine Sciences, Beijing 100053, China.
authors: Run-Shun
authors: RS
authors: Zhang
authors: Guanganmen Hospital, China Academy of Chinese Medicine Sciences, Beijing 100053, China.
authors: Ying-Hui
authors: YH
authors: Wang
authors: School of Computing, Informatics and Media, University of Bradford, Bradford BD7 1DP, UK.
authors: Yonghong
authors: Y
authors: Peng
authors: China Academy of Chinese Medicine Sciences, Beijing 100700, China ; Institute of Basic Theory of Traditional Chinese Medicine, China Academy of Chinese Medicine Sciences, Beijing 100700, China.
authors: Jing-Qing
authors: JQ
authors: Hu
authors: China Academy of Chinese Medicine Sciences, Beijing 100700, China.
authors: Qi
authors: Q
authors: Xie
authors: Guanganmen Hospital, China Academy of Chinese Medicine Sciences, Beijing 100053, China.
authors: Yan-Xing
authors: YX
authors: Xue
authors: Guanganmen Hospital, China Academy of Chinese Medicine Sciences, Beijing 100053, China.
authors: Li-Li
authors: LL
authors: Xu
authors: Dongfang Hospital, Beijing 100078, China.
authors: Xiao-Fang
authors: XF
authors: Liu
authors: China Academy of Chinese Medicine Sciences, Beijing 100700, China.
authors: Bao-Yan
authors: BY
authors: Liu
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