Hashemian M , Knowles D , Calver J , et al . iEpi:an end to end solution for collecting,conditioning and utilizing epidemiologically relevant data . Proceedings of the 2nd ACM International Workshop on Pervasive Wireless Healthcare New York,USA , 2012 : 3 ～ 8

Snijders C , Matzat U , Reips U D . Big data:big gaps of knowledge in the field of internet science . International Journal of Internet Science , 2012 , 7 ( 1 ): 1 ～ 5

Sharma S , Mangat V . Technology and trends to handle big data:survey . Proceedings of the 5th International Conference on Advanced Computing& Communication Technologies (ACCT) , Haryana,India , 2015 : 266 ～ 271

Kelly J . Big data vendor revenue and market forecast . http://www.kdnuggets.com/2014/04/big-data-vendor-analysis-clusters.html http://www.kdnuggets.com/2014/04/big-data-vendor-analysis-clusters.html , 2014

Nambiar R , Bhardwaj R , Sethi A , et al . A look at challenges and opportunities of big data analytics in healthcare . Proceedings of 2013 IEEE International Conference on Big Data , Silicon Valley,California,USA , 2012 : 17 ～ 22

Groves P , KayyaliB , KnottD , et al . The big data revolution in healthcare . McKinsey and Company , 2013 : 1 ～ 19

Manyika J , Chui M , Brown B , et al . Big Data:the Next Frontier for Innovation,Competition,and Productivity . Report of McKinsey Global Institute , 2011

Jacobs A . The pathologies of big data . Communications of the ACM , 2009 , 52 ( 8 ): 36 ～ 44 .

中国医院协会信息管理专业委员会 . 中国医院信息化状况调查报告--2006公开版 . 中国数字医学 , 2007 , 2 ( 2 ): 5 ～ 15

China Hospital Information Management Association . China Hospital IT Application Survey Report,2006 Public Edition . China Digital Medicine , 2007 , 2 ( 2 ): 5 ～ 15

颜延 , 秦兴彬 , 樊建平 et al . 医疗健康大数据研究综述 . 科研信息化技术与应用 , 2014 , 5 ( 6 ): 3 ～ 16

Yan Y , Qin X B , Fan J P , et al . A review of big data research in medicine ＆healthcare . E-Science Technology ＆Application , 2014 , 5 ( 6 ): 3 ～ 16

Davidson M W , Haim D A , Radin J M . Using Networks to Combine “Big Data”and Traditional Surveillance toImproveInfluenza Predictions . Scientific Reports , 2015 ( 5 ): 1 ～ 5

Chunara R , Aman S , Smolinski M , et al . Flu near you:an online self-reported influenza surveillance system in the USA . Online Journal of Public Health Informatics , 2013 , 5 ( 1 )

Ginsberg J , Mohebbi M H , Patel R S , et al . Detecting influenza epidemics using search engine query data . Nature , 2009 , 457 ( 7232 ): 1012 ～ 1014

Polgreen P M , Chen Y L , Pennock D M , et al . Using internet searches for influenza surveillance . ClinicalInfectious Diseases , 2008 , 47 ( 11 ): 1443 ～ 1448

Lampos V , Bie T D , Cristianini N . Flu detector-tracking epidemics on twitter . Machine Learning and Knowledge Discovery in Databases , 2010 ( 6323 ): 599 ～ 602

Hashemian M S , Stanley K G , Osgood N . Flunet:automated tracking of contacts during flu season . Proceedings of the 8th International Symposium on Modeling and Optimization in Mobile,Ad Hoc and Wireless Networks (WiOpt) , Avignon,France , 2010 : 384 ～ 353

Salathé M , Kazandjieva M , Lee J W , et al . A high-resolution human contact network for infectious disease transmission . Proceedings of the National Academy of Sciences , California,USA , 2010 : 22020 ～ 22025

Hashemian M S , Stanley K G , Knowles D L , et al . Human network data collection in the wild:the epidemiological utility of micro-contact and location data . Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium , New York,USA , 2012 : 255 ～ 264

Kostkova P . A roadmap to integrated digital public health surveillance:the vision and the challenges . Proceedings of the 22nd International Conference on World Wide Web Companion , London,UK , 2013

Karimi S , Wang C , Jimenez A M , et al . Text and data mining techniques in adverse drug reaction detection . ACM Computing Surveys , 2015 , 47 ( 4 ): 56

Jin H D , Chen J , He H X , et al . Mining unexpected temporal associations:applications in detecting adverse drug reactions . IEEE Transactions on Information Technology in Biomedicine , 2008 , 12 ( 4 ): 488 ～ 500

Chazard E , Ficheur G , Bernonville S , et al . Data mining to generate adverse drug events detection rules . IEEE Transactions on Information Technology in Biomedicine , 2011 , 15 ( 6 ): 823 ～ 830

Ji Y Q , Hao Y , Tran J , et al . A method for mining infrequent causal associations and its application in finding adverse drug reaction signal pairs . IEEE Transactions on Knowledge and Data Engineering , 2013 , 25 ( 4 ): 721 ～ 733

Ji Y Q , Hao Y , Dews P , et al . A fuzzy recognition-primed decision model-based causal association mining algorithm for detecting adverse drug reactions in postmarketing surveillance . Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ) , Barcelona,Spain , 2010 : 1 ～ 8

Ji Y Q , Hao Y , Dews P , et al . A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance . IEEE Transactions on Information Technology in Biomedicine , 2011 , 15 ( 3 ): 428 ～ 437

Kohn L T , Corrigan J , Donaldson M S . To Err is Human:Building a Safer Health System . Washington DC : National Academies Press , 2000

Chen H , Compton S , Hsiao O . DiabeticLink:a Health Big Data System for Patient Empowerment and Personalized Healthcare . Smart Health . Berlin Heidelberg:Springer , 2013

Srinivasan U , Arunasalam B . Leveraging big data analytics to reduce healthcare costs . IT Professional , 2013 , 15 ( 6 ): 21 ～ 28

Molnar L K . Nanobioinformatics:the enabling technology of personalized medicine . Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering . Boston,USA , 2007

Andreu P J , Leff D , Ip H , et al . From wearable sensors to smart implants-towards pervasive and personalised healthcare . IEEE Transactions on Biomedical Engineering , 2015

Panahiazar M , Taslimitehrani V , Jadhav A , et al . Empowering personalized medicine with big data and semantic web technology:promises,challenges,and use cases . Proceedings of IEEE International Conference on Big Data , Washington DC,USA , 2014 : 790 ～ 795

Yesha Y , Janeja V P , Rishe N , et al . Personalized decision support system to enhance evidence based medicine through big data analytics . Proceedings of IEEE International Conference on Healthcare Informatics , Verona,Italy , 2014

Chuang J , Hsiao O , Wu P L , et al . DiabeticLink:an Integrated and Intelligent Cyber-Enabled Health Social Platform for Diabetic Patients . Smart Health.Berlin Heidelberg:Springer , 2014

Kim T W , Park K H , Yi S H , et al . A big data framework for u-healthcare systems utilizing vital signs . Proceedings of International Symposium on Computer,Consumer and Control (IS3C) , Taiwan,China , 2014 : 494 ～ 497

Schrenker R A . Guest editor’s introduction:software engineering for future healthcare and clinical systems . Computer , 2006 ( 4 ): 26 ～ 32

Shneiderman B , Plaisant C , Hesse B W . Improving healthcare with interactive visualization . Computer , 2013 , 46 ( 5 ): 58 ～ 66

Ginsburg G S , Willard H F . Genomic and personalized medicine:foundations and applications . Translational Research , 2009 , 154 ( 6 ): 277 ～ 287

Douali N , Jaulent M . Genomic and personalized medicine decision support system . Proceedings of International Conference on Complex Systems (ICCS) , Agadir,Morocco , 2012 : 1 ～ 4

Matturdi B , Zhou X W , Li S , et al . Big data security and privacy:a review . Communications , 2014 , 11 ( 14 ): 135 ～ 145