Overview
- Editors:
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Josiah Poon
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University of Sydney, Sydney, Australia
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Simon K. Poon
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University of Sydney, Sydney, Australia
- Presents a data analytic approach for an efficient way to analyze the data, to find useful patterns, to generate and validate hypothesis
- Offers data mining researchers a new domain of study, an area which sits on a wealth of data untouched for development of new algorithms to address the specific nature of this field
- Provides the biostatistics community and health practitioners a means to analyze Traditional Chinese Medicine (TCM)
- Includes supplementary material: sn.pub/extras
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Table of contents (13 chapters)
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- Simon K. Poon, Shagun Goyal, Albert Cheng, Josiah Poon
Pages 1-16
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- Simon K. Poon, Alan Su, Lily Chau, Josiah Poon
Pages 17-38
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- Yi Sun, Qi Liu, Zhiwei Cao
Pages 81-96
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- Guang Zheng, Miao Jiang, Cheng Lu, Aiping Lu
Pages 97-109
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- Yan Zhao, Nevin L. Zhang, Tianfang Wang, Qingguo Wang, Tengfei Liu
Pages 111-121
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- Zhimin Zhang, Yizeng Liang, Peishan Xie, Footim Chau, Kelvin Chan
Pages 133-153
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- Foo-tim Chau, Qing-song Xu, Daniel Man-yuen Sze, Hoi-yan Chan, Tsui-yan Lau, Da-lin Yuan et al.
Pages 155-172
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- Chun-Hay Ko, Lily Chau, David Wing-Shing Cheung, Johnny Chi-Man Koon, Kwok-Pui Fung, Ping-Chung Leung et al.
Pages 173-188
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- Xuezhong Zhou, Baoyan Liu, Xiaoping Zhang, Qi Xie, Runshun Zhang, Yinghui Wang et al.
Pages 189-213
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- Jing Yang, Hua Su, Guoshun Tang, Zihan Zheng, Yue Shen, Lei Zhang et al.
Pages 215-226
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- 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.
