Clinical research of traditional Chinese medicine in big data era

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.

[1]  L. Nelson Data, data everywhere. , 1997, Critical care medicine.

[2]  Joseph M. Hellerstein,et al.  MAD Skills: New Analysis Practices for Big Data , 2009, Proc. VLDB Endow..

[3]  J. Jones,et al.  Life in the 21st century - a vision for all. , 1998, South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde.

[4]  Jones Js,et al.  Life in the 21st century - a vision for all. , 1998 .

[5]  Sarah Parish,et al.  Randomized trial of intravenous streptokinase, oral aspirin, both, or neither among 17,187 cases of suspected acute myocardial infarction: ISIS-2.ISIS-2 (Second International Study of Infarct Survival) Collaborative Group. , 1988, Journal of the American College of Cardiology.

[6]  Tony Hey,et al.  The Fourth Paradigm: Data-Intensive Scientific Discovery , 2009 .

[7]  L. Bolognese,et al.  RANDOMISED TRIAL OF INTRAVENOUS STREPTOKINASE, ORAL ASPIRIN, BOTH, OR NEITHER AMONG 17 187 CASES OF SUSPECTED ACUTE MYOCARDIAL INFARCTION: ISIS-2 , 1988, The Lancet.

[8]  Meng Xiaofeng and Ci Xiang,et al.  Big Data Management: Concepts,Techniques and Challenges , 2013 .

[9]  Baoyan Liu,et al.  Qi-Shen-Yi-Qi Dripping Pills for the Secondary Prevention of Myocardial Infarction: A Randomised Clinical Trial , 2013, Evidence-based complementary and alternative medicine : eCAM.

[10]  R. Pyeritz The Marfan syndrome. , 1986, American family physician.

[11]  Craven Ll Prevention of coronary and cerebral thrombosis. , 1956 .

[12]  P. Glasziou Aspirin after myocardial infarction , 1989, Lancet.

[13]  Li Zhan-hua Research progress and trends of big data from a database perspective , 2013 .

[14]  R Peto,et al.  Why do we need some large, simple randomized trials? , 1984, Statistics in medicine.

[15]  Leroy Hood,et al.  Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. , 2004, Journal of proteome research.

[16]  J. Worrall Do We Need Some Large, Simple Randomized Trials in Medicine? , 2010 .