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Clinical research of traditional Chinese medicine in big data era

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Abstract

With the advent of big data era, our thinking, technology and methodology are being transformed. Data-intensive scientific discovery based on big data, named “The Fourth Paradigm,” has become a new paradigm of scientific research. Along with the development and application of the Internet information technology in the field of healthcare, individual health records, clinical data of diagnosis and treatment, and genomic data have been accumulated dramatically, which generates big data in medical field for clinical research and assessment. With the support of big data, the defects and weakness may be overcome in the methodology of the conventional clinical evaluation based on sampling. Our research target shifts from the “causality inference” to “correlativity analysis.” This not only facilitates the evaluation of individualized treatment, disease prediction, prevention and prognosis, but also is suitable for the practice of preventive healthcare and symptom pattern differentiation for treatment in terms of traditional Chinese medicine (TCM), and for the post-marketing evaluation of Chinese patent medicines. To conduct clinical studies involved in big data in TCM domain, top level design is needed and should be performed orderly. The fundamental construction and innovation studies should be strengthened in the sections of data platform creation, data analysis technology and big-data professionals fostering and training.

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References

  1. WHO. Life in the 21st Century: a vision for all. World Health Rep 2008

    Google Scholar 

  2. Weston AD, Hood L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Proteome Res 2004; 3(2): 179–196

    Article  PubMed  CAS  Google Scholar 

  3. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ 2008; 337: a1655

    Article  PubMed  PubMed Central  Google Scholar 

  4. Craven LL. Prevention of coronary thrombosis and acetylsalicylic acid. Miss Valley Med J 1956; 78: 213–215

    PubMed  CAS  Google Scholar 

  5. Editorial. Aspirin after myocardial infarction. Lancet 1980; 315(8179): 1172–1173

    Google Scholar 

  6. ISIS-2 (Second International Study of Infarct Survival) Collaborative Group. Randomised trial of intravenous streptokinase, oral aspirin, both, or neither among 17,187 cases of suspected acute myocardial infarction: ISIS-2. Lancet 1988; 332(8607): 349–360

    Article  Google Scholar 

  7. Yusuf S, Collins R, Peto R. Why do we need some large, simple randomized trials? Stat Med 1984; 3(4): 409–422

    Article  PubMed  CAS  Google Scholar 

  8. Worrall J. Do We Need Some Large, Simple Randomized Trials in Medicine? EPSA Philosophical Issues in the Sciences. London, UK: Springer, 2010

    Google Scholar 

  9. Nature. Big Data[EB/OL]. 2012. http://www.nature.com/news/specials/bigdata/index.html

  10. Office of Science and Technology Policy. Big Data across the Federal Government. March 29, 2012

  11. Li ZH, Wang GR, Zhou AY. Research progress and trends of big data from a database perspective. Comput Eng Sci (Ji Suan Ji Ke Xue Yu Ji Shu) 2013; 35(10): 1–11 (in Chinese)

    Google Scholar 

  12. Hey T, Tansley S, Tolle K. The Fourth Paradigm: data-intensive scientific discovery. Microsoft, October 16, 2009

    Google Scholar 

  13. China Hospital Information Management Association. Chinese Hospital Informationization Development Research Report (white paper). 2008 (in Chinese)

    Google Scholar 

  14. Zhou GH, Xin Y, Zhang YJ, Hu T, Li YF. Study on big data’s applications in medical and health field. Chin J Health Inf Manag (Zhongguo Wei Sheng Xin Xi Guan Li Za Zhi) 2013; 110(4): 296–300, 304 (in Chinese)

    Google Scholar 

  15. http://www.1000genomes.org/ (Accessed August 10, 2014)

  16. http://aws.amazon.com/cn/1000genomes/ (Accessed August 10, 2014)

  17. Xu DQ, Yang HQ. The application of big data on healthcare personalized service. Chin J Health Inf Manag (Zhongguo Wei Sheng Xin Xi Guan Li Za Zhi) 2013; 110(4): 301–304 (in Chinese)

    Google Scholar 

  18. Cohen J, Dolan B, Dunlap M, Hellerstein JM, Welton C. MAD skills: new analysis practices for big data. PVLDB 2009; 2(2): 1481–1492

    Google Scholar 

  19. Song Y, Wang DY. Challenges and opportunities of clinical research in the big data era. J Med Postgra (Yi Xue Yan Jiu Sheng Xue Bao) 2014; 27(4): 337–339 (in Chinese)

    Google Scholar 

  20. Shang H, Zhang J, Yao C, Liu B, Gao X, Ren M, Cao H, Dai G, Weng W, Zhu S, Wang H, Xu H, Zhang B. Qi-shen-yi-qi dripping pills for the secondary prevention of myocardial infarction: a randomised clinical trial. Evid Based Complement Alternat Med 2013; 2013: 738391

    PubMed  PubMed Central  Google Scholar 

  21. http://www.consort-statement.org/ (Accessed August 10, 2014)

  22. Li P. Three transitions of hospital informatization in the era of cloud computing and big data. Chin Hosp Manag (Zhongguo Yi Yuan Guan Li) 2013; 33(12): 80–81 (in Chinese)

    Google Scholar 

  23. Liu BY. Clinical research paradigm of traditional Chinese medicine in the real world. J Tradit Chin Med (Zhong Yi Za Zhi) 2013; 54(6): 451–455 (in Chinese)

    Google Scholar 

  24. Meng XF, Ci X. Big data management: concepts, techniques and challenges. J Comput Res Dev (Ji Suan Ji Yan Jiu Yu Fa Zhan) 2013; 50(1): 146–169 (in Chinese)

    Google Scholar 

  25. Data, data everywhere. The Economist Feb 25th 2010. http://www.economist.com/node/15557443

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Correspondence to Boli Zhang.

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Zhang, J., Zhang, B. Clinical research of traditional Chinese medicine in big data era. Front. Med. 8, 321–327 (2014). https://doi.org/10.1007/s11684-014-0370-y

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