Symptom network topological features predict the effectiveness of herbal treatment for pediatric cough

  • Mengxue Huang
  • Jingjing Wang
  • Runshun Zhang
  • Zhuying Ni
  • Xiaoying Liu
  • Wenwen Liu
  • Weilian Kong
  • Yao Chen
  • Tiantian Huang
  • Guihua Li
  • Dan WeiEmail author
  • Jianzhong LiuEmail author
  • Xuezhong ZhouEmail author
Research Article


Pediatric cough is a heterogeneous condition in terms of symptoms and the underlying disease mechanisms. Symptom phenotypes hold complicated interactions between each other to form an intricate network structure. This study aims to investigate whether the network structure of pediatric cough symptoms is associated with the prognosis and outcome of patients. A total of 384 cases were derived from the electronic medical records of a highly experienced traditional Chinese medicine (TCM) physician. The data were divided into two groups according to the therapeutic effect, namely, an invalid group (group A with 40 cases of poor efficacy) and a valid group (group B with 344 cases of good efficacy). Several well-established analysis methods, namely, statistical test, correlation analysis, and complex network analysis, were used to analyze the data. This study reports that symptom networks of patients with pediatric cough are related to the effectiveness of treatment: a dense network of symptoms is associated with great difficulty in treatment. Interventions with the most different symptoms in the symptom network may have improved therapeutic effects.


pediatric cough complex network symptoms traditional Chinese medicine electronic medical records 


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This work was supported by the National Key R&D Program (No. 2004BA721A01Z73) and the National Key Research and Development Project of China (No. 2017YFC1703506).

Supplementary material

11684_2019_699_MOESM1_ESM.pdf (104 kb)
Supplementary material, approximately 106 KB.


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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Mengxue Huang
    • 1
  • Jingjing Wang
    • 2
  • Runshun Zhang
    • 3
  • Zhuying Ni
    • 4
    • 5
  • Xiaoying Liu
    • 4
    • 5
  • Wenwen Liu
    • 2
  • Weilian Kong
    • 2
  • Yao Chen
    • 4
    • 5
  • Tiantian Huang
    • 6
  • Guihua Li
    • 6
  • Dan Wei
    • 4
    • 5
    Email author
  • Jianzhong Liu
    • 4
    • 5
    Email author
  • Xuezhong Zhou
    • 2
    Email author
  1. 1.Shanghai Traditional Chinese Medicine-Integrated HospitalShanghaiChina
  2. 2.Institute of Medical Intelligence, School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina
  3. 3.Guang’anmen HospitalChina Academy of Chinese Medical SciencesBeijingChina
  4. 4.Hubei Provincial Hospital of Traditional Chinese MedicineWuhanChina
  5. 5.Hubei Province Academy of Traditional Chinese MedicineWuhanChina
  6. 6.School of the Clinical Medicine College of Traditional Chinese MedicineHubei University of Chinese MedicineWuhanChina

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