Frontiers of Medicine

, Volume 12, Issue 2, pp 206–217 | Cite as

Multistage analysis method for detection of effective herb prescription from clinical data

  • Kuo Yang
  • Runshun Zhang
  • Liyun He
  • Yubing Li
  • Wenwen Liu
  • Changhe Yu
  • Yanhong Zhang
  • Xinlong Li
  • Yan Liu
  • Weiming Xu
  • Xuezhong Zhou
  • Baoyan Liu
Research Article


Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g., insomnia). First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb–symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples) compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.


effective prescription detection herb set enrichment analysis core network extraction insomnia personalized treatment 


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This work is partially supported by the National Natural Science Foundation of China (Nos. 61105055 and 81230086), the National Basic Research Program of China (No. 2014CB542903), the Special Programs of Traditional Chinese Medicine (Nos. 201407001, JDZX2015170, and JDZX2015171), and the National Key Technology R&D Program (Nos. 2013BAI02B01 and 2013BAI13B04). We thank Xue Xu and Lu Zhang for their valuable discussions.


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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  • Kuo Yang
    • 1
  • Runshun Zhang
    • 2
  • Liyun He
    • 3
  • Yubing Li
    • 1
  • Wenwen Liu
    • 1
  • Changhe Yu
    • 3
  • Yanhong Zhang
    • 3
  • Xinlong Li
    • 3
  • Yan Liu
    • 4
  • Weiming Xu
    • 5
  • Xuezhong Zhou
    • 1
    • 4
  • Baoyan Liu
    • 4
  1. 1.School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and MiningBeijing Jiaotong UniversityBeijingChina
  2. 2.Guanganmen HospitalChina Academy of Chinese Medical SciencesBeijingChina
  3. 3.Institute of Basic Clinical MedicineChina Academy of Chinese Medical SciencesBeijingChina
  4. 4.Data Center of Traditional Chinese MedicineChina Academy of Chinese Medical SciencesBeijingChina
  5. 5.Institute of Basic Theory of Traditional Chinese MedicineChina Academy of Chinese Medical SciencesBeijingChina

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