Please wait a minute...
Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2014, Vol. 8 Issue (3) : 321-327     DOI: 10.1007/s11684-014-0370-y
RESEARCH ARTICLE |
Clinical research of traditional Chinese medicine in big data era
Junhua Zhang1,Boli Zhang1,2,*()
1. Tianjin University of Traditional Chinese Medicine, Tianjin 210029, China
2. China Academy of Chinese Medical Sciences, Beijing 100700, China
Download: PDF(106 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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     
Corresponding Authors: Boli Zhang   
Online First Date: 16 September 2014    Issue Date: 09 October 2014
URL:  
http://academic.hep.com.cn/fmd/EN/10.1007/s11684-014-0370-y     OR     http://academic.hep.com.cn/fmd/EN/Y2014/V8/I3/321
1 WHO. Life in the 21st Century: a vision for all. World Health Rep2008
2 Weston AD, Hood L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Proteome Res2004; 3(2): 179–196
doi: 10.1021/pr0499693 pmid: 15113093
3 Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ2008; 337: a1655
4 Craven LL. Prevention of coronary thrombosis and acetylsalicylic acid. Miss Valley Med J1956; 78: 213–215
pmid: 13358612
5 Editorial. Aspirin after myocardial infarction. Lancet1980; 315(8179): 1172–1173
pmid: 6103990
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. Lancet1988; 332(8607): 349–360
pmid: 2899772
7 Yusuf S, Collins R, Peto R. Why do we need some large, simple randomized trials? Stat Med1984; 3(4): 409–422
doi: 10.1002/sim.4780030421 pmid: 6528136
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. <month>March</month><day> 29</day>, 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)
12 Hey T, Tansley S, Tolle K. The Fourth Paradigm: data-intensive scientific discovery. Microsoft, <month>October</month><day> 16</day>, 2009
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 http://www.1000genomes.org/ (Accessed <month>August</month><day> 10</day>, 2014)
16 http://aws.amazon.com/cn/1000genomes/ (Accessed <month>August</month><day> 10</day>, 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)
18 Cohen J, Dolan B, Dunlap M, Hellerstein JM, Welton C. MAD skills: new analysis practices for big data. PVLDB2009; 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 Med2013; 2013: 738391
doi: 10.1155/2013/738391 pmid: 23935677
21 http://www.consort-statement.org/ (Accessed <month>August</month> 10<month></month>, 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)
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. http://www.economist.com/node/15557443
[1] Yunfang Liu,Zhiping Yang,Jing Cheng,Daiming Fan. Barriers and countermeasures in developing traditional Chinese medicine in Europe[J]. Front. Med., 2016, 10(3): 360-376.
[2] Yan Ma,Kehua Zhou,Jing Fan,Shuchen Sun. Traditional Chinese medicine: potential approaches from modern dynamical complexity theories[J]. Front. Med., 2016, 10(1): 28-32.
[3] Yan Ma,Shuchen Sun,Chung-Kang Peng. Applications of dynamical complexity theory in traditional Chinese medicine[J]. Front. Med., 2014, 8(3): 279-284.
[4] Jian Wang,Biyan Liang,Xiaoping Zhang,Liran Xu,Xin Deng,Xiuhui Li,Lu Fang,Xinghua Tan,Yuxiang Mao,Guoliang Zhang,Yuguang Wang. An 84-month observational study of the changes in CD4 T-lymphocyte cell count of 110 HIV/AIDS patients treated with traditional Chinese medicine[J]. Front. Med., 2014, 8(3): 362-367.
[5] Guozheng Li,Xuewen Zuo,Baoyan Liu. Scientific computation of big data in real-world clinical research[J]. Front. Med., 2014, 8(3): 310-315.
[6] Zhuyuan Fang,Xiaowei Fan,Gong Chen. A study on specialist or special disease clinics based on big data[J]. Front. Med., 2014, 8(3): 376-381.
[7] Yixin Zhong,Baoyan Liu,Hua Qu,Qi Xie. Methodological challenges to human medical study[J]. Front. Med., 2014, 8(3): 328-336.
[8] Li Ma,Baoyan Liu,Qi Xie,Shusong Mao,Zhiwei Cui. Ontological reconstruction of the clinical terminology of traditional Chinese medicine[J]. Front. Med., 2014, 8(3): 358-361.
[9] Runshun Zhang,Yinghui Wang,Baoyan Liu,Guangli Song,Xuezhong Zhou,Shizhen Fan,Xishui Pan. Clinical data quality problems and countermeasure for real world study[J]. Front. Med., 2014, 8(3): 352-357.
[10] Xuezhong Zhou,Yubing Li,Yonghong Peng,Jingqing Hu,Runshun Zhang,Liyun He,Yinghui Wang,Lijie Jiang,Shiyan Yan,Peng Li,Qi Xie,Baoyan Liu. Clinical phenotype network: the underlying mechanism for personalized diagnosis and treatment of traditional Chinese medicine[J]. Front. Med., 2014, 8(3): 337-346.
[11] Guanli Song,Yinghui Wang,Runshun Zhang,Baoyan Liu,Xuezhong Zhou,Xiaji Zhou,Hong Zhang,Yufeng Guo,Yanxing Xue,Lili Xu. Experience inheritance from famous specialists based on real-world clinical research paradigm of traditional Chinese medicine[J]. Front. Med., 2014, 8(3): 300-309.
[12] Ping Liu, Songlin Liu, Gang Chen, Ping Wang. Understanding channel tropism in traditional Chinese medicine in the context of systems biology[J]. Front Med, 2013, 7(3): 277-279.
[13] Bingxue Shang, Zhifei Cao, Quansheng Zhou. Progress in tumor vascular normalization for anticancer therapy: challenges and perspectives[J]. Front Med, 2012, 6(1): 67-78.
[14] Miao Jiang, Cheng Xiao, Gao Chen, Cheng Lu, Qinglin Zha, Xiaoping Yan, Weiping Kong, Shijie Xu, Dahong Ju, Pu Xu, Youwen Zou, Aiping Lu. Correlation between cold and hot pattern in traditional Chinese medicine and gene expression profiles in rheumatoid arthritis[J]. Front Med, 2011, 5(2): 219-228.
[15] Changhua Liu, Man Gu. Protecting traditional knowledge of Chinese medicine: concepts and proposals[J]. Front Med, 2011, 5(2): 212-218.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed