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中华临床实验室管理电子杂志 ›› 2017, Vol. 05 ›› Issue (01) : 30 -35. doi: 10.3877/cma.j.issn.2095-5820.2017.01.008

所属专题: 文献

专题笔谈

大数据的现状、机遇与挑战
汪浩1,()   
  1. 1. 510330 广州金域医学检验中心
  • 收稿日期:2017-01-23 出版日期:2017-02-28
  • 通信作者: 汪浩

Big data of current state, opportunity and challenge

Hao Wang1,()   

  1. 1. Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou 510005, China
  • Received:2017-01-23 Published:2017-02-28
  • Corresponding author: Hao Wang
  • About author:
    Corresponding author: Wang Hao, Email:
引用本文:

汪浩. 大数据的现状、机遇与挑战[J]. 中华临床实验室管理电子杂志, 2017, 05(01): 30-35.

Hao Wang. Big data of current state, opportunity and challenge[J]. Chinese Journal of Clinical Laboratory Management(Electronic Edition), 2017, 05(01): 30-35.

大数据的数据量非常巨大,以至传统数据储存和计算等技术无法有效地进行数据处理,且产生和变化的速度快、种类繁杂,数据的真实性也有很大的不确定性。在医疗数字化的过程中,医院成了大数据产生的重要来源,病历、影像、远程医疗等都会产生大量的数据。对医疗卫生行业而言,大数据正在传统的商业智能和临床决策支持系统的基础上,延伸至人们将医疗网络产生的大数据应用到循证医疗中,从而助力精准医疗、健康管理和智慧医疗,甚至生物样本库的建立和应用中。本文主要阐述现阶段大数据的组成与特征,大数据分析处理的框架和方法,同时分析医疗大数据的发展机遇和应用中面临的挑战。

Big data refers to the data sets that have become so large and/or complex that traditional data technology is inadequate to process them effectively. Big data has large volume; changes quickly; has great variety; and has a good deal of uncertainty in its veracity. During the digitization of healthcare, hospitals become important sources of big data. Large volume of data is created from medical records, medical images, and distance medicine. For healthcare industry, big data analytics are evolving beyond traditional business intelligence and clinical decision support systems. It utilizes the vast amount of data gathered from healthcare networks for evidence-based medicine, propels precision medicine, health management, disease prevention, even the establishment and application of biobanks. This paper summarizes the current composition, characteristics, and trend of big data. It introduces the framework, tools, and methods for big data analytics.

图1 现阶段大数据分析处理应用的框架构成图
图2 社会医疗成本分布图
图3 现阶段大数据的主要挑战示意图
1
Khan N, Yaqoob I, Hashem IA, et al. Big Data: Survey, technologies, opportunities, and challenges[J]. Sci World J, 2014,2014:712826.
2
Sagiroglu S, Sinanc D. Big data: A review. Collaboration technologies and systems (CTS)[R]. San Diego:IEEE, 2013.
3
Laney D. 3D data management: controlling data volume, velocity and variety. Meta Group Res Note, 2001:1-4.
4
Pawar AM. Big Data mining: challenges, technologies, tools and applications[J]. Database Syst J, 2016,2:28-33.
5
Reed D, Dongarra J. Exascale computing and big data[J]. Commun ACM, 2015, 58(7):56-68.
6
Tsai CF, Lin WC, Ke SW. Big data mining with parallel computing: A comparison of distributed and MapReduce methodologies[J]. J Syst Softw, 2016,122:83-92.
7
Qiu JF, Wu QH, Ding GR, et al. A survey of machine learning for big data processing[J]. EURASIP J Adv Signal Proc, 2016(1):67.
8
Guru P, Nagesh H, Prabhu S. High performance computation of big data: performance optimization approach towards a parallel frequent item set mining algorithm for transaction data based on Hadoop MapReduce framework[J]. IJISA, 2017,1:75-84.
9
Wang Y, Kung LA, Burd TA. Big data analytics: understanding its capabilities and potential benefits forhealthcare organizations[J]. Technol Forecast Soc, 2016. (forthcoming)
10
Lyu Y, Xie Q, Zheng B, et al. Insights and thinking from big data of cervical examination and human papillomavirus test[J]. Chin J Clin Lab Mgt, 2016,4(1):8-12.
11
Simeondubach D, Watson P. Biobanking 3.0: evidence based and customer focused biobanking[J]. Clin Biochem, 2014,47(4-5):300-308.
12
Grauholm J, Khoo SK, Nicholov RZ, et al. Gene expression profiling of archived dried blood spot samples from the Danish Neonatal Screening Biobank[J]. Mol Genet Metab, 2015,116(3):119-124.
13
Laisk-Podar T, Lindgren C, Peters M, et al. Ovarian physiology and GWAS: bionanks, biology, and beyond[J]. Trends Endocrinol Metab, 2016,27(7):516-528.
14
Ravid R. Biobanks for biomarkers in neurological disorders: the Da Vinci bridge for optimal clinico-pathological connection[J]. J Neurol Sci, 2009,283(1-2):119-126.
15
Branković I, Malogajski J, Morré SA. Biobanking and translation of human genetics and genomics for infectious diseases[J]. App Transl Genom, 2014,3(2):30-35.
16
Krysiak-Baltyn K, Nordahl Petersen T, Audouze K, et al. Compass: a hybrid method for clinical and biobank data mining[J]. J Biomed Inform, 2014,47:160-170.
17
Welikala RA, Fraz MM, Foster PJ, et al. Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies[J]. Comput Biol Med, 2016,71:67-76.
18
Singh B, Kumar S, Kaur G, et al. A survey on big data: challenges, tools and technique[J]. IJARCET, 2016,7(6): 230-234.
19
Sivarajah U, Kamal MM, Irani Z, et al. Critical analysis of big data challenges and analytical methods[J]. J Bus Res, 2017,70:263-286.
20
Kaur K. Big Data barriers and opportunities[J]. IJARCS, 2016,7(6):263-265.
21
Siddiqa A, Hashem IAT, Yaqoob I, et al. A survey of big data management: Taxonomy and state-of-the-art[J]. J Netw Comput Appl, 2016,71:151-166.
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