Clinical research of traditional Chinese medicine in big data era

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.

Keywords

big data / traditional Chinese medicine / clinical evaluation / evidence based medicine

References

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

[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

[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

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

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

[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

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

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

[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. 2012-March- 29

[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)

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

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

[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)

[15] 1000genomes.org website

[16] amazon.com website

[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)

[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

[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)

[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

[21] consort-statement.org website

[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)

[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)

[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)

[25] Data, data everywhere. The Economist Feb 25th 2010.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 81102733) and the Program for New Century Excellent Talent of Ministry of Education of China (NCET-13-0936).

Compliance with ethics guidelines

Junhua Zhang and Boli Zhang declare that they have no conflict of interest. This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg