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A Network-Based Approach to Investigate the Pattern of Syndrome in Depression
Song, Jianglong1; Liu, Xi1; Deng, Qingqiong2; Dai, Wen1; Gao, Yibo1; Chen, Lin1; Zhang, Yunling3; Wang, Jialing3; Yu, Miao3; Lu, Peng1; Guo, Rongjuan3
Source PublicationEvidence-based Complementary and Alternative Medicine
AbstractIn 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 naive 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 naive 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.
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Indexed BySCI
WOS Research AreaIntegrative & Complementary Medicine
WOS SubjectIntegrative & Complementary Medicine
WOS IDWOS:000353510200001
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Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China
3.Beijing Univ Chinese Med, Dongfang Hosp, Beijing 100029, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Song, Jianglong,Liu, Xi,Deng, Qingqiong,et al. A Network-Based Approach to Investigate the Pattern of Syndrome in Depression[J]. Evidence-based Complementary and Alternative Medicine,2015.
APA Song, Jianglong.,Liu, Xi.,Deng, Qingqiong.,Dai, Wen.,Gao, Yibo.,...&Guo, Rongjuan.(2015).A Network-Based Approach to Investigate the Pattern of Syndrome in Depression.Evidence-based Complementary and Alternative Medicine.
MLA Song, Jianglong,et al."A Network-Based Approach to Investigate the Pattern of Syndrome in Depression".Evidence-based Complementary and Alternative Medicine (2015).
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