1 Stewart B W, Wild C P. World Cancer Report 2014. Geneva: WHO Press, 2014
[2]
2 Chen W, Zheng R, Zhang S, et al. Report of cancer incidence and mortality in China, 2010. Ann Transl Med, 2014, 2: 61
[3]
3 Taktak A F G, Fisher A C. Outcome Prediction in Cancer. Amsterdam: Elsevier Science Publisers, 2007
[4]
4 Ripley R M, Harris A L, Tarassenko L. Non-linear survival analysis using neural networks. Stat Med, 2004, 23: 825-842
[5]
5 Biganzoli E M, Boracchi P, Ambrogi F, et al. Artificial neural network for the joint modelling of discrete cause-specific hazards. Artif Intell Med, 2006, 37: 119-130
[6]
6 Bishop J B, Szpalski M, Ananthraman S K, et al. Classification of low back pain from dynamic motion characteristics using an artificial neural network. Spine, 1997, 22: 2991-2998
[7]
7 Hu X, Cammann H, Meyer H A, et al. Artificial neural networks and prostate cancer—tools for diagnosis and management. Nat Rev Urol, 2013, 10: 174-182
[8]
8 Ahmed F E. Artificial neural networks for diagnosis and survival prediction in colon cancer. Mol Cancer, 2005, 4: 29
[9]
9 Burke H B. Artificial neural networks for cancer research: Outcome prediction. Semin Surg Oncol, 1994, 10: 73-79
[10]
10 Fridley B L, Koestler D C, Godwin A K. Individualizing care for ovarian cancer patients using big data. J Natl Cancer Inst, 2014, doi:10.1093/jnci/dju080
[11]
11 De Neve W, De Gersem W, Madani I. Rational use of intensity-modulated radiation therapy: The importance of clinical outcome. Semin Radiat Oncol, 2012, 22: 40-49
[12]
12 Veldeman L, Madani I, Hulstaert F, et al. Evidence behind use of intensity-modulated radiotherapy: A systematic review of comparative clinical studies. Lancet Oncol, 2008, 9: 367-375
[13]
13 Wang S D. Opportunities and challenges of clinical research in the big-data era: From RCT to BCT. J Thorac Dis, 2013, 5: 721-723
[14]
14 Sibbald B, Roland M. Understanding controlled trials. Why are randomised controlled trials important? Br Med J, 1998, 316: 201
[15]
15 Nallamothu B K, Hayward R A, Bates E R. Beyond the randomized clinical trial: The role of effectiveness studies in evaluating cardiovascular therapies. Circulation, 2008, 118: 1294-1303
[16]
16 Meyer A M, Wheeler S B, Weinberger M, et al. An overview of methods for comparative effectiveness research. Semin Radiat Oncol, 2014, 24: 5-13
[17]
17 Viktor M S, Kenneth C. Sheng Y Y, Zhou T, Translated. Big data: A Revolution That Will Transform How We Live, Work, and Think (in Chinese). Hangzhou: Zhejiang People's Publishing House, 2013 [维克托·迈尔-舍恩伯格, 肯尼思·库克耶, 著. 盛杨燕, 周涛, 译. 大数据时代: 生活、工作和思维的大变革. 杭州: 浙江人民出版社,
[18]
18 Rajaraman A, Ullman J. Mining of Massive Datasets. Cambridge: Cambridge University Press, 2012
[19]
59 Saeed M, Villarroel M, Reisner A T, et al. Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database. Crit Care Med, 2011, 39: 952-960
[20]
60 Zhang Z, Xu X, Ni H, et al. Urine output on ICU entry is associated with hospital mortality in unselected critically ill patients. J Nephrol, 2014, 27: 65-71
[21]
61 Zhang Z, Xu X, Ni H, et al. Predictive value of ionized calcium in critically ill patients: An analysis of a large clinical database MIMIC II. PLoS One, 2014, 9: e95204
[22]
62 Saeed M, Lieu C, Raber G, et al. MIMIC II: A massive temporal ICU patient database to support research in intelligent patient monitoring. Comput Cardiol, 2002, 29: 641-644
[23]
63 Mikhno A, Ennett C M. Prediction of extubation failure for neonates with respiratory distress syndrome using the MIMIC-II clinical database. Conf Proc IEEE Eng Med Biol Soc, 2012, 2012: 5094-5097
[24]
64 Scott D J, Lee J, Silva I, et al. Accessing the public MIMIC-II intensive care relational database for clinical research. BMC Med Inform Decis Mak, 2013, 13: 9
[25]
65 Lee J, Scott D J, Villarroel M, et al. Open-access MIMIC-II database for intensive care research. Conf Proc IEEE Eng Med Biol Soc, 2011, 2011: 8315-8318
[26]
66 Cooke C R, Iwashyna T J. Using existing data to address important clinical questions in critical care. Crit Care Med, 2013, 41: 886-896
[27]
67 Margolis R, Derr L, Dunn M, et al. The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: Capitalizing on biomedical big data. J Am Med Inform Assoc, 2014, 21: 957-958
[28]
68 Psaty B M, Breckenridge A M. Mini-sentinel and regulatory science—big data rendered fit and functional. N Engl J Med, 2014, 370: 2165-2167
[29]
69 Schneeweiss S. Learning from big health care data. N Engl J Med, 2014, 370: 2161-2163
[30]
70 Zhang Z, Xu X, Ni H, et al. Platelet indices are novel predictors of hospital mortality in intensive care unit patients. J Crit Care, 2014, 29: 885.e1-e6
[31]
71 Khoury M J, Gwinn M L, Glasgow R E, et al. A population approach to precision medicine. Am J Prev Med, 2012, 42: 639-645
[32]
19 Mohammed E A, Far B H, Naugler C. Applications of the MapReduce programming framework to clinical big data analysis: Current landscape and future trends. BioData Min, 2014, 7: 22
[33]
20 Kapiteijn E, Marijnen C A, Nagtegaal I D, et al. Preoperative radiotherapy combined with total mesorectal excision for resectable rectal cancer. N Engl J Med, 2001, 345: 638-646
[34]
21 Bosset J F, Collette L, Calais G, et al. Chemotherapy with preoperative radiotherapy in rectal cancer. N Engl J Med, 2006, 355: 1114-1123
[35]
22 Gerard J P, Conroy T, Bonnetain F, et al. Preoperative radiotherapy with or without concurrent fluorouracil and leucovorin in T3-4 rectal cancers: Results of FFCD 9203. J Clin Oncol, 2006, 24: 4620-4625
[36]
23 Sauer R, Becker H, Hohenberger W, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med, 2004, 351: 1731-1740
[37]
24 Fernandes L, O'Connor M, Weaver V. Big data, bigger outcomes: Healthcare is embracing the big data movement, hoping to revolutionize HIM by distilling vast collection of data for specific analysis. J AHIMA, 2012, 83: 38-43
[38]
25 Big Data and Analytics Key to Accountable Care Success. IDC Health Insights, 2012
[39]
26 Marx V. Biology: The big challenges of big data. Nature, 2013, 498: 255-260
[40]
27 Savage N. Bioinformatics: Big data versus the big C. Nature, 2014, 509: S66-S67
[41]
28 Community cleverness required. Nature, 2008, 455: 1
[42]
29 Shaikh A R, Prabhu Das I, Vinson C A, et al. Cyberinfrastructure for consumer health. Am J Prev Med, 2011, 40: S91-S96
[43]
30 Murphy S, Patlak M. A Foundation for Evidence-Driven Practice: A Rapid Learning System for Cancer Care: Workshop Summary. Washington: National Academies Press, 2010
[44]
31 Butte A J, Shah N H. Computationally translating molecular discoveries into tools for medicine: Translational bioinformatics articles now featured in JAMIA. J Am Med Inform Assoc, 2011, 18: 352-353
[45]
32 Dalton W S, Sullivan D M, Yeatman T J, et al. The 2010 Health Care Reform Act: A potential opportunity to advance cancer research by taking cancer personally. Clin Cancer Res, 2010, 16: 5987-5996
[46]
33 Shaikh A R, Butte A J, Schully S D, et al. Collaborative biomedicine in the age of big data: The case of cancer. J Med Internet Res, 2014, 16: e101
[47]
34 Coulouris G, Dollimore J, Kindberg T. Distributed systems: Concepts and design. IEEE/ACM J Netw, 2005, 18: 182-231
[48]
35 de Oliveira Branco M. Distributed Data Management for Large Scale Applications. Southampton: University of Southampton, 2009
[49]
36 Zou Q, Li X B, Jiang W R, et al. Survey of MapReduce frame operation in bioinformatics. Brief Bioinform, 2014, 15: 637-647
[50]
37 Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Commun ACM, 2008, 51: 7
[51]
38 O'Driscoll A, Daugelaite J, Sleator R D. “Big data”, Hadoop and cloud computing in genomics. J Biomed Inform, 2013, 46: 774-781
[52]
39 Dong X, Bahroos N, Sadhu E, et al. Leverage hadoop framework for large scale clinical informatics applications. AMIA Jt Summits Transl Sci Proc, 2013, 2013: 53
[53]
40 Stella F, Amer Y. Continuous time Bayesian network classifiers. J Biomed Inform, 2012, 45: 1108-1119
[54]
41 Bellazzi R. Big data and biomedical informatics: A challenging opportunity. Yearb Med Inform, 2014, 9: 8-13
[55]
42 Wolfe P J. Making sense of big data. Proc Natl Acad Sci USA, 2013, 110: 18031-18032
[56]
43 Wall D P, Kudtarkar P, Fusaro V A, et al. Cloud computing for comparative genomics. BMC Bioinformatics, 2010, 11: 259
[57]
44 Kudtarkar P, Deluca T F, Fusaro V A, et al. Cost-effective cloud computing: A case study using the comparative genomics tool, roundup. Evol Bioinform Online, 2010, 6: 197-203
[58]
45 Lin C W, Abdul S S, Clinciu D L, et al. Empowering village doctors and enhancing rural healthcare using cloud computing in a rural area of mainland China. Comput Methods Programs Biomed, 2014, 113: 585-592
[59]
46 Kaur P D, Chana I. Cloud based intelligent system for delivering health care as a service. Comput Methods Programs Biomed, 2014, 113: 346-359
[60]
47 Zhou S, Liao R, Guan J. When cloud computing meets bioinformatics: A review. J Bioinform Comput Biol, 2013, 11: 1330002
[61]
48 Raghupathi W, Raghupathi V. Big data analytics in healthcare: Promise and potential. Health Inf Sci Syst, 2014, 2: 3
[62]
49 Davoli T, Xu A W, Mengwasser K E, et al. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome. Cell, 2013, 155: 948-962
[63]
50 Adzhubei I A, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods, 2010, 7: 248-249
[64]
51 Cancer Genome Atlas Research N. Comprehensive molecular characterization of gastric adenocarcinoma. Nature, 2014, 513: 202-209
[65]
52 Venet D, Dumont J E, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol, 2011, 7: e1002240
[66]
53 Roy J, Winter C, Isik Z, et al. Network information improves cancer outcome prediction. Brief Bioinform, 2014, 15: 612-625
[67]
54 van't Veer L J, Dai H, van de Vijver M J, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 2002, 415: 530-536
[68]
55 Chuang H Y, Lee E, Liu Y T, et al. Network-based classification of breast cancer metastasis. Mol Syst Biol, 2007, 3: 140
[69]
56 Johannes M, Brase J C, Frohlich H, et al. Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients. Bioinformatics, 2010, 26: 2136-2144
[70]
57 Winter C, Kristiansen G, Kersting S, et al. Google goes cancer: Improving outcome prediction for cancer patients by network-based ranking of marker genes. PLoS Comput Biol, 2012, 8: e1002511
[71]
58 Nibbe R K, Koyuturk M, Chance M R. An integrative-omics approach to identify functional sub-networks in human colorectal cancer. PLoS Comput Biol, 2010, 6: e1000639
[72]
72 Fenstermacher D A, Wenham R M, Rollison D E, et al. Implementing personalized medicine in a cancer center. Cancer J, 2011, 17: 528-536
[73]
73 Steinberg G B, Church B W, McCall C J, et al. Novel predictive models for metabolic syndrome risk: A “big data” analytic approach. Am J Manag Care, 2014, 20: e221-e228
[74]
74 Meyer A M, Olshan A F, Green L, et al. Big data for population-based cancer research: The integrated cancer information and surveillance system. N C Med J, 2014, 75: 265-269
[75]
75 Wheeler S B, Wu Y, Meyer A M, et al. Use and timeliness of radiation therapy after breast-conserving surgery in low-income women with early-stage breast cancer. Cancer Invest, 2012, 30: 258-267
[76]
76 Carpenter W R, Tyree S, Wu Y, et al. A surveillance system for monitoring, public reporting, and improving minority access to cancer clinical trials. Clin Trials, 2012, 9: 426-435
[77]
77 Holmes J A, Carpenter W R, Wu Y, et al. Impact of distance to a urologist on early diagnosis of prostate cancer among black and white patients. J Urol, 2012, 187: 883-888
[78]
78 Mays G P, Smith S A. Evidence links increases in public health spending to declines in preventable deaths. Health Aff (Millwood), 2011, 30: 1585-1593
[79]
79 Wheeler S B, Kohler R E, Goyal R K, et al. Is medical home enrollment associated with receipt of guideline-concordant follow-up care among low-income breast cancer survivors? Med Care, 2013, 51: 494-502
[80]
80 Kuo T M, Mobley L R, Anselin L. Geographic disparities in late-stage breast cancer diagnosis in California. Health Place, 2011, 17: 327-334
[81]
81 Methodology Committee of the Patient-Centered Outcomes Research I. Methodological standards and patient-centeredness in comparative effectiveness research: The PCORI perspective. JAMA, 2012, 307: 1636-1640
[82]
82 Luce B R, Kramer J M, Goodman S N, et al. Rethinking randomized clinical trials for comparative effectiveness research: The need for transformational change. Ann Intern Med, 2009, 151: 206-209
[83]
83 Schilsky R L, Michels D L, Kearbey A H, et al. Building a rapid learning health care system for oncology: The regulatory framework of CancerLinQ. J Clin Oncol, 2014, 32: 2373-2379
[84]
84 Masters G A, Krilov L, Bailey H H, et al. Clinical cancer advances 2015: Annual report on progress against cancer from the American Society of Clinical Oncology. J Clin Oncol, 2015, 33: 786-809
[85]
85 Rehm H L, Berg J S, Brooks L D, et al. ClinGen—the Clinical Genome Resource. N Engl J Med, 2015, 372: 2235-2242
[86]
86 Koskan A, Klasko L, Davis S N, et al. Use and taxonomy of social media in cancer-related research: A systematic review. Am J Public Health, 2014, 104: e20-e37
[87]
87 Chew C, Eysenbach G. Pandemics in the age of Twitter: Content analysis of Tweets during the 2009 H1N1 outbreak. PLoS One, 2010, 5: e14118
[88]
88 Broniatowski D A, Paul M J, Dredze M. National and local influenza surveillance through Twitter: An analysis of the 2012-2013 influenza epidemic. PLoS One, 2013, 8: e83672
[89]
89 Lazer D, Kennedy R, King G, et al. Big data. The parable of Google Flu: Traps in big data analysis. Science, 2014, 343: 1203-1205
[90]
90 Young S D. A “big data” approach to HIV epidemiology and prevention. Prev Med, 2015, 70C: 17-18
[91]
91 Young S D. Behavioral insights on big data: Using social media for predicting biomedical outcomes. Trends Microbiol, 2014, 22: 601-602
[92]
92 Zaid T, Burzawa J, Basen-Engquist K, et al. Use of social media to conduct a cross-sectional epidemiologic and quality of life survey of patients with neuroendocrine carcinoma of the cervix: A feasibility study. Gynecol Oncol, 2014, 132: 149-153
[93]
93 Khoury M J, Ioannidis J P. Medicine. Big data meets public health. Science, 2014, 346: 1054-1055
[94]
94 Devaraj S, Ow T, Kohli R. Examining the impact of information technology and patient flow on healthcare performance: A Theory of Swift and Even Flow (TSEF) perspective. J Oper Manag, 2013, 31: 12
[95]
95 Han Z Y, Zhen T M, Gu J L, et al. 3521 project-based master design of regional health information construction framework. Chin J Med Libr Inform Sci, 2014, 23: 4
