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Data Mining in Real-World Traditional Chinese Medicine Clinical Data Warehouse

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Data Analytics for Traditional Chinese Medicine Research

Abstract

Real-world clinical setting is the major arena of traditional Chinese medicine (TCM) as it has experienced long-term practical clinical activities, and developed established theoretical knowledge and clinical solutions suitable for personalized treatment. Clinical phenotypes have been the most important features captured by TCM for diagnoses and treatment, which are diverse and dynamically changeable in real-world clinical settings. Together with clinical prescription with multiple herbal ingredients for treatment, TCM clinical activities embody immense valuable data with high dimensionalities for knowledge distilling and hypothesis generation. In China, with the curation of large-scale real-world clinical data from regular clinical activities, transforming the data to clinical insightful knowledge has increasingly been a hot topic in TCM field. This chapter introduces the application of data warehouse techniques and data mining approaches for utilizing real-world TCM clinical data, which is mainly from electronic medical records. The main framework of clinical data mining applications in TCM field is also introduced with emphasizing on related work in this field. The key points and issues to improve the research quality are discussed and future directions are proposed.

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Notes

  1. 1.

    http://en.wikipedia.org/wiki/Metabolic_syndrome (Accessed: 2013 June 30th).

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Acknowledgements

This work is partially supported by National 863 Program of China (2012AA02A609), NSFC Project (61105055, 81230086), China 973 Project (2014CB542900), National S&T Major Project of China (2009ZX09301-005-010, 2009ZX09502-031) and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Xuezhong Zhou

  2. China Academy of Chinese Medical Sciences, Beijing, 100700, China

    Baoyan Liu, Xiaoping Zhang & Qi Xie

  3. Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China

    Runshun Zhang & Yinghui Wang

  4. School of Computing, Informatics and Media, University of Bradford, Bradford, BD7 1DP, UK

    Yonghong Peng

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  1. University of Sydney, Sydney, New South Wales, Australia

    Josiah Poon

  2. University of Sydney, Sydney, New South Wales, Australia

    Simon K. Poon

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Zhou, X. et al. (2014). Data Mining in Real-World Traditional Chinese Medicine Clinical Data Warehouse. In: Poon, J., K. Poon, S. (eds) Data Analytics for Traditional Chinese Medicine Research. Springer, Cham. https://doi.org/10.1007/978-3-319-03801-8_11

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