title: A network-based approach to investigate the pattern of syndrome in depression.
abstract: In Traditional Chinese Medicine theory, syndrome is essential to diagnose diseases and treat patients, and symptom is the foundation of syndrome differentiation. Thus the combination and interaction between symptoms represent the pattern of syndrome at phenotypic level, which can be modeled and analyzed using complex network. At first, we collected inquiry information of 364 depression patients from 2007 to 2009. Next, we learned classification models for 7 syndromes in depression using naïve Bayes, Bayes network, support vector machine (SVM), and C4.5. Among them, SVM achieves the highest accuracies larger than 0.9 except for Yin deficiency. Besides, Bayes network outperforms naïve Bayes for all 7 syndromes. Then key symptoms for each syndrome were selected using Fisher's score. Based on these key symptoms, symptom networks for 7 syndromes as well as a global network for depression were constructed through weighted mutual information. Finally, we employed permutation test to discover dynamic symptom interactions, in order to investigate the difference between syndromes from the perspective of symptom network. As a result, significant dynamic interactions were quite different for 7 syndromes. Therefore, symptom networks could facilitate our understanding of the pattern of syndrome and further the improvement of syndrome differentiation in depression.
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Jianglong
authors.identifier: 0000-0001-5697-9412
authors.initials: J
authors.lastname: Song
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Xi
authors.initials: X
authors.lastname: Liu
authors.affiliation: College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.
authors.forename: Qingqiong
authors.initials: Q
authors.lastname: Deng
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Wen
authors.initials: W
authors.lastname: Dai
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Yibo
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Chen
authors.affiliation: Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100029, China.
authors.forename: Yunling
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100029, China.
authors.forename: Jialing
authors.initials: J
authors.lastname: Wang
authors.affiliation: Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100029, China.
authors.forename: Miao
authors.initials: M
authors.lastname: Yu
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Peng
authors.identifier: 0000-0002-9288-9075
authors.initials: P
authors.lastname: Lu
authors.affiliation: Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100029, China.
authors.forename: Rongjuan
authors.initials: R
authors.lastname: Guo
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