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Evidence-Based Complementary and Alternative Medicine
Volume 2015 (2015), Article ID 768249, 11 pages
Research Article

A Network-Based Approach to Investigate the Pattern of Syndrome in Depression

1Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
3Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100029, China

Received 30 September 2014; Revised 15 January 2015; Accepted 19 January 2015

Academic Editor: Shun-Wan Chan

Copyright © 2015 Jianglong Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.