© 2014

Data Analytics for Traditional Chinese Medicine Research

  • Josiah Poon
  • Simon K. Poon

Table of contents

  1. Front Matter
    Pages i-xii
  2. Simon K. Poon, Shagun Goyal, Albert Cheng, Josiah Poon
    Pages 1-16
  3. Mingyu You, Guo-Zheng Li
    Pages 39-79
  4. Yi Sun, Qi Liu, Zhiwei Cao
    Pages 81-96
  5. Guang Zheng, Miao Jiang, Cheng Lu, Aiping Lu
    Pages 97-109
  6. Yan Zhao, Nevin L. Zhang, Tianfang Wang, Qingguo Wang, Tengfei Liu
    Pages 111-121
  7. Zhimin Zhang, Yizeng Liang, Peishan Xie, Footim Chau, Kelvin Chan
    Pages 133-153
  8. Foo-tim Chau, Qing-song Xu, Daniel Man-yuen Sze, Hoi-yan Chan, Tsui-yan Lau, Da-lin Yuan et al.
    Pages 155-172
  9. Chun-Hay Ko, Lily Chau, David Wing-Shing Cheung, Johnny Chi-Man Koon, Kwok-Pui Fung, Ping-Chung Leung et al.
    Pages 173-188
  10. Xuezhong Zhou, Baoyan Liu, Xiaoping Zhang, Qi Xie, Runshun Zhang, Yinghui Wang et al.
    Pages 189-213
  11. Jing Yang, Hua Su, Guoshun Tang, Zihan Zheng, Yue Shen, Lei Zhang et al.
    Pages 215-226
  12. Kelvin Chan, Josiah Poon, Simon K. Poon, Miao Jiang, Aiping Lu
    Pages 227-248

About this book


This contributed volume explores how data mining, machine learning, and similar statistical techniques can analyze the types of problems arising from Traditional Chinese Medicine (TCM) research. The book focuses on the study of clinical data and the analysis of herbal data. Challenges addressed include diagnosis, prescription analysis, ingredient discoveries, network based mechanism deciphering, pattern-activity relationships, and medical informatics. Each author demonstrates how they made use of machine learning, data mining, statistics and other analytic techniques to resolve their research challenges, how successful if these techniques were applied, any insight noted and how these insights define the most appropriate future work to be carried out. Readers are given an opportunity to understand the complexity of diagnosis and treatment decision, the difficulty of modeling of efficacy in terms of herbs, the identification of constituent compounds in an herb, the relationship between these compounds and biological outcome so that evidence-based predictions can be made. Drawing on a wide range of experienced contributors, Data Analytics for Traditional Chinese Medicine Research is a valuable reference for professionals and researchers working in health informatics and data mining. The techniques are also useful for biostatisticians and health practitioners interested in traditional medicine and data analytics.


Chemometrics Clinical data Data analytics Data warehouse Evidence-based Herbal network Interaction Pattern-activity relationship Synergy Traditional Chinese Medicine

Editors and affiliations

  • Josiah Poon
    • 1
  • Simon K. Poon
    • 2
  1. 1.University of SydneySydneyAustralia
  2. 2.University of SydneySydneyAustralia

Bibliographic information