Progress and Application of Medical Data Mining under the Background of Big Data_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

1. Department of Evidence-Based Medicine and Clinical Epidemiology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding?author：

Keywords：

Big data; Data mining; Medical research

DOI：

10.7507/1007-4848.20160013

Video：

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The era of big data has brought a big revolution that will transform the way we live, work, and think. In medical field, as the development of social economics and medicine since 21 century, the human disease spectrum has been changing, the disease type has been increasing, and the complexity of the etiology, diagnosis and treatment of disease have been gradually increasing. In order to improve the healthy level, and explore the law of disease occurrence and development, we should constantly research to find discipline in enormous knowledge by fully mining and using the big medical data. It will be helpful to improve the level medical information management. And it can be supportive to the diagnosis, treatment, clinical practice and decision-making. We did the review under the background of big data, and the mean contact of this review is about the origin, meaning, classification, features of big data as well as the research process, application and future development of data mining, especially clinical data mining.

Citation： QINWen-zhe, CHENJin, DONGLi. Progress and Application of Medical Data Mining under the Background of Big Data. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2016, 23(1): 55-60. doi: 10.7507/1007-4848.20160013 Copy

1.	李國杰.大數據研究的科學價值.中國計算機學會通訊, 2012, 8(9): 8-15.
2.	Gantz J, Reinsel D. Extracting value from chaos. IDC iview, 2011, 11429-10.
3.	李國杰, 程學旗.大數據研究:未來科技及經濟社會發展的重大戰略領域——大數據的研究現狀與科學思考.中國科學院院刊, 2012, 27(6): 647-657.
4.	潘璠.大數據的概念并非無解.青海統計, 2014, 10: 1-1.
5.	Madden S. From databases to big data. IEEE Internet Computing, 2012, 3: 4-6.
6.	Chen M, Mao SW, Liu YH. Big data: A survey. Mobile Networks and Applications, 2014, 19(2): 171-209.
7.	劉維責. "大數據"研究綜述.辦公自動化:綜合月刊, 2014, 1: 27-30.
8.	賀憲民, 吳騁, 于長春, 等.數據挖掘技術在醫學領域中的應用.第二軍醫大學學報, 2004, 24(11): 1250-1252.
9.	張永明.數字圖書館個性化信息服務研究.河南圖書館學刊, 2010, 2(3): 30-31.
10.	夏虹.探析數據挖掘在建筑企業管理中的應用.城市建設理論研究(電子版), 2013, 13(9): 211-212.
11.	Benson DA, Karsch-Mizrachi I, Lipman DJ, et al. GenBank. Nucl Aci Res, 2005, 33(suppl 1): D34-D38.
12.	Srimani PK, Koti MS. A comparison of different learning models used in data mining for medical data; proceedings of the 2ndinterna-tionalconferenceonmethodsandmodelsinscienceandtechnology. AIP Publishing, 2011, 1414(1):51-55.
13.	Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet, 2012, 13(6): 395-405.
14.	Cios KJ, Moore GW. Uniqueness of medical data mining. Artif Intell Med, 2002, 26(1): 1-24.
15.	Zhang Z. Big data and clinical research: focusing on the area of critical care medicine in mainland China. Quant ImagMed Surg, 2014, 4(5): 426.
16.	Wang SD. Opportunities and challenges of clinical research in the big-data era: from RCT to BCT. J Thorac Dis, 2013, 5(6): 721-723.
17.	Bath PA. Data mining in health and medical information. Annual review of information science and technology, 2004, 38(1): 331-369.
18.	滕琪, 樊小毛, 何晨光, 等.醫療大數據特征挖掘及重大突發疾病早期預警.網絡新媒體技術, 2014, (1): 50-54.
19.	李頌.大數據改變生活.決策與信息, 2014, 374(10): 12-13.
20.	劉娟.基于數據挖掘的分類預測模型研究.現代商業, 2014, (12): 47-47.
21.	Consortium IWP, TEK, RBA, et al. Estimation of the warfarin dose with clinical and pharmacogenetic data. N Engl JMed, 2009, 360(8): 753-764.
22.	Naimi A, Westreich DJ. Big data: a revolution that will transform how we live, work, and think. Am J Epidemoil, 2014 April 23.[Epub ahead of print].
23.	Saxena A, Newcomb AE, Dhurandhar V, et al. Application of clinical databases to contemporary cardiac surgery practice: where are we now? Heart, Lung and Circ, 2015, 19(2): S1443-9506.
24.	Welke KF, O'brien SM, Peterson ED, et al. The complex relationship between pediatric cardiac surgical case volumes and mortality rates in a national clinical database. J Thorac Cardiovasc Surg, 2009, 137(5): 1133-1140.
25.	Pasquali SK, He X, Jacobs JP, et al. Evaluation of failure to rescue as a quality metric in pediatric heart surgery: an analysis of the STS congenital heart surgery database. Ann Thorac Surg, 2012, 94(2): 573-580.
26.	Jacobs JP, O'brien SM, Pasquali SK, et al. The importance of patient-specific preoperative factors: an analysis of the society of thoracic surgeons congenital heart surgery database. Ann Thorac Surg, 2014, 98(5): 1653-1659.
27.	Dibardino DJ, Pasquali SK, Hirsch JC, et al. Effect of sex and race on outcome in patients undergoing congenital heart surgery: an analysis of the society of thoracic surgeons congenital heart surgery database. Ann Thorac Surg, 2012, 94(6): 2054-2060.
28.	付博, 董力, 石應康.中國人心臟瓣膜置換術后抗凝治療數據庫建設.中國胸心血管外科臨床雜志, 2013, 20(1): 2-9.
29.	王劍, 張政波, 王衛東, 等.基于重癥監護數據庫MIMIC-Ⅱ的臨床數據挖掘研究.中國醫療器械雜志, 2014, 38(6): 402-406.

1. 李國杰.大數據研究的科學價值.中國計算機學會通訊, 2012, 8(9): 8-15.
2. Gantz J, Reinsel D. Extracting value from chaos. IDC iview, 2011, 11429-10.
3. 李國杰, 程學旗.大數據研究:未來科技及經濟社會發展的重大戰略領域——大數據的研究現狀與科學思考.中國科學院院刊, 2012, 27(6): 647-657.
4. 潘璠.大數據的概念并非無解.青海統計, 2014, 10: 1-1.
5. Madden S. From databases to big data. IEEE Internet Computing, 2012, 3: 4-6.
6. Chen M, Mao SW, Liu YH. Big data: A survey. Mobile Networks and Applications, 2014, 19(2): 171-209.
7. 劉維責. "大數據"研究綜述.辦公自動化:綜合月刊, 2014, 1: 27-30.
8. 賀憲民, 吳騁, 于長春, 等.數據挖掘技術在醫學領域中的應用.第二軍醫大學學報, 2004, 24(11): 1250-1252.
9. 張永明.數字圖書館個性化信息服務研究.河南圖書館學刊, 2010, 2(3): 30-31.
10. 夏虹.探析數據挖掘在建筑企業管理中的應用.城市建設理論研究(電子版), 2013, 13(9): 211-212.
11. Benson DA, Karsch-Mizrachi I, Lipman DJ, et al. GenBank. Nucl Aci Res, 2005, 33(suppl 1): D34-D38.
12. Srimani PK, Koti MS. A comparison of different learning models used in data mining for medical data; proceedings of the 2ndinterna-tionalconferenceonmethodsandmodelsinscienceandtechnology. AIP Publishing, 2011, 1414(1):51-55.
13. Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet, 2012, 13(6): 395-405.
14. Cios KJ, Moore GW. Uniqueness of medical data mining. Artif Intell Med, 2002, 26(1): 1-24.
15. Zhang Z. Big data and clinical research: focusing on the area of critical care medicine in mainland China. Quant ImagMed Surg, 2014, 4(5): 426.
16. Wang SD. Opportunities and challenges of clinical research in the big-data era: from RCT to BCT. J Thorac Dis, 2013, 5(6): 721-723.
17. Bath PA. Data mining in health and medical information. Annual review of information science and technology, 2004, 38(1): 331-369.
18. 滕琪, 樊小毛, 何晨光, 等.醫療大數據特征挖掘及重大突發疾病早期預警.網絡新媒體技術, 2014, (1): 50-54.
19. 李頌.大數據改變生活.決策與信息, 2014, 374(10): 12-13.
20. 劉娟.基于數據挖掘的分類預測模型研究.現代商業, 2014, (12): 47-47.
21. Consortium IWP, TEK, RBA, et al. Estimation of the warfarin dose with clinical and pharmacogenetic data. N Engl JMed, 2009, 360(8): 753-764.
22. Naimi A, Westreich DJ. Big data: a revolution that will transform how we live, work, and think. Am J Epidemoil, 2014 April 23.[Epub ahead of print].
23. Saxena A, Newcomb AE, Dhurandhar V, et al. Application of clinical databases to contemporary cardiac surgery practice: where are we now? Heart, Lung and Circ, 2015, 19(2): S1443-9506.
24. Welke KF, O'brien SM, Peterson ED, et al. The complex relationship between pediatric cardiac surgical case volumes and mortality rates in a national clinical database. J Thorac Cardiovasc Surg, 2009, 137(5): 1133-1140.
25. Pasquali SK, He X, Jacobs JP, et al. Evaluation of failure to rescue as a quality metric in pediatric heart surgery: an analysis of the STS congenital heart surgery database. Ann Thorac Surg, 2012, 94(2): 573-580.
26. Jacobs JP, O'brien SM, Pasquali SK, et al. The importance of patient-specific preoperative factors: an analysis of the society of thoracic surgeons congenital heart surgery database. Ann Thorac Surg, 2014, 98(5): 1653-1659.
27. Dibardino DJ, Pasquali SK, Hirsch JC, et al. Effect of sex and race on outcome in patients undergoing congenital heart surgery: an analysis of the society of thoracic surgeons congenital heart surgery database. Ann Thorac Surg, 2012, 94(6): 2054-2060.
28. 付博, 董力, 石應康.中國人心臟瓣膜置換術后抗凝治療數據庫建設.中國胸心血管外科臨床雜志, 2013, 20(1): 2-9.
29. 王劍, 張政波, 王衛東, 等.基于重癥監護數據庫MIMIC-Ⅱ的臨床數據挖掘研究.中國醫療器械雜志, 2014, 38(6): 402-406.

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