{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:03:37Z","timestamp":1785456217826,"version":"3.56.0"},"reference-count":311,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T00:00:00Z","timestamp":1611187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T00:00:00Z","timestamp":1611187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00530-020-00736-8","type":"journal-article","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T02:02:56Z","timestamp":1611194576000},"page":"1339-1371","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":212,"title":["Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities"],"prefix":"10.1007","volume":"28","author":[{"given":"Arshia","family":"Rehman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeeda","family":"Naz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imran","family":"Razzak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,21]]},"reference":[{"key":"736_CR1","unstructured":"Frost, S.: Drowning in big data? reducing information technology complexities and costs for healthcare organizations (2015)"},{"issue":"1","key":"736_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/2047-2501-2-3","volume":"2","author":"W Raghupathi","year":"2014","unstructured":"Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 3 (2014)","journal-title":"Health Inf. Sci. Syst."},{"key":"736_CR3","doi-asserted-by":"crossref","unstructured":"Baro, E., Degoul, S., Beuscart, R., Chazard, E.: Toward a literature-driven definition of big data in healthcare. BioMed Res. Int. (2015)","DOI":"10.1155\/2015\/639021"},{"key":"736_CR4","unstructured":"Burghard, C.: Big data and analytics key to accountable care success. In: IDC Health Insights pp. 1\u20139 (2012)"},{"issue":"12","key":"736_CR5","first-page":"2012","volume":"11","author":"A Dembosky","year":"2012","unstructured":"Dembosky, A.: Data prescription for better healthcare. Financial Times 11(12), 2012 (2012)","journal-title":"Financial Times"},{"key":"736_CR6","unstructured":"Feldman, B., Martin, E.M., Skotnes, T.: Big data in healthcare hype and hope. Dr. Bonnie 360, 122\u2013125 (2012)"},{"issue":"10","key":"736_CR7","first-page":"38","volume":"83","author":"LM Fernandes","year":"2012","unstructured":"Fernandes, L.M., O\u2019Connor, M., Weaver, V.: Big data, bigger outcomes. J. AHIMA 83(10), 38\u201343 (2012)","journal-title":"J. AHIMA"},{"issue":"2","key":"736_CR8","doi-asserted-by":"publisher","first-page":"e1003904","DOI":"10.1371\/journal.pcbi.1003904","volume":"11","author":"E Vayena","year":"2015","unstructured":"Vayena, E., Salath\u00e9, M., Madoff, L.C., Brownstein, J.S.: Ethical challenges of big data in public health. PLoS Comput. Biol. 11(2), e1003904 (2015)","journal-title":"PLoS Comput. Biol."},{"issue":"3","key":"736_CR9","doi-asserted-by":"publisher","first-page":"203","DOI":"10.2471\/BLT.14.139022","volume":"93","author":"R Wyber","year":"2015","unstructured":"Wyber, R., Vaillancourt, S., Perry, W., Mannava, P., Folaranmi, T., Celi, L.A.: Big data in global health: improving health in low-and middle-income countries. Bull. World Health Org. 93(3), 203\u2013208 (2015)","journal-title":"Bull. World Health Org."},{"issue":"5","key":"736_CR10","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1016\/j.bushor.2014.06.003","volume":"57","author":"MJ Ward","year":"2014","unstructured":"Ward, M.J., Marsolo, K.A., Froehle, C.M.: Applications of business analytics in healthcare. Bus. Horizons 57(5), 571\u2013582 (2014)","journal-title":"Bus. Horizons"},{"issue":"2","key":"736_CR11","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1111\/anae.12537","volume":"69","author":"DI Sessler","year":"2014","unstructured":"Sessler, D.I.: Big data-and its contributions to peri-operative medicine. Anaesthesia 69(2), 100\u2013105 (2014)","journal-title":"Anaesthesia"},{"key":"736_CR12","unstructured":"Razzak, M.I., Imran, M., Xu, G.: Big data analytics for preventive medicine. Neural Comput. Appl., 1\u201335 (2019)"},{"key":"736_CR13","unstructured":"Ericsson Mobility Report February Interim 2018. https:\/\/www.ericsson.com\/491b06\/assets\/local\/mobility-report\/documents\/2019\/ericsson-mobility-report-q4-2019-update.pdf (2018)"},{"key":"736_CR14","unstructured":"Available:: Internet world stats. https:\/\/www.internetworldstats.com\/stats.htm (2018)"},{"issue":"2\u20134","key":"736_CR15","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1504\/IJBET.2017.087722","volume":"25","author":"G Manogaran","year":"2017","unstructured":"Manogaran, G., Lopez, D.: A survey of big data architectures and machine learning algorithms in healthcare. Int. J. Biomed. Eng. Technol. 25(2\u20134), 182\u2013211 (2017)","journal-title":"Int. J. Biomed. Eng. Technol."},{"issue":"7502","key":"736_CR16","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1038\/nature13319","volume":"509","author":"M Wilhelm","year":"2014","unstructured":"Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A.M., Lieberenz, M., Savitski, M.M., Ziegler, E., Butzmann, L., Gessulat, S., Marx, H., et al.: Mass-spectrometry-based draft of the human proteome. Nature 509(7502), 582 (2014)","journal-title":"Nature"},{"issue":"6","key":"736_CR17","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1097\/00001888-199906000-00012","volume":"74","author":"MJ Ackerman","year":"1999","unstructured":"Ackerman, M.J.: The visible human project: a resource for education. Acad. Med. 74(6), 667\u2013670 (1999)","journal-title":"Acad. Med."},{"key":"736_CR18","doi-asserted-by":"crossref","unstructured":"Gui, H., Zheng, R., Ma, C., Fan, H., Xu, L.: An architecture for healthcare big data management and analysis. In: International Conference on Health Information Science, pp. 154\u2013160. Springer (2016)","DOI":"10.1007\/978-3-319-48335-1_17"},{"key":"736_CR19","doi-asserted-by":"crossref","unstructured":"Razzak, M.I., Naz, S., Zaib, A.: Deep learning for medical image processing: Overview, challenges and the future. In: Classification in BioApps, pp. 323\u2013350. Springer (2018)","DOI":"10.1007\/978-3-319-65981-7_12"},{"key":"736_CR20","unstructured":"Cox, M., Ellsworth, D.: Application-controlled demand paging for out-of-core visualization. In: Proceedings of the 8th Conference on Visualization\u201997, pp. 235\u2013ff. IEEE Computer Society Press (1997)"},{"issue":"1","key":"736_CR21","first-page":"25","volume":"10","author":"A Kocha\u0144ski","year":"2010","unstructured":"Kocha\u0144ski, A.: Data preparation. Comput. Methods Mater. Sci. 10(1), 25\u201329 (2010)","journal-title":"Comput. Methods Mater. Sci."},{"key":"736_CR22","unstructured":"Diebold, F.X.: Big data dynamic factor models for macroeconomic measurement and forecasting. In: Advances in Economics and Econometrics: Theory and Applications, Eighth World Congress of the Econometric Society, \u201d(edited by M. Dewatripont, LP Hansen and S. Turnovsky), pp. 115\u2013122 (2003)"},{"issue":"70","key":"736_CR23","first-page":"1","volume":"6","author":"D Laney","year":"2001","unstructured":"Laney, D.: 3d data management: Controlling data volume, velocity and variety. META Group Res. Note 6(70), 1 (2001)","journal-title":"META Group Res. Note"},{"key":"736_CR24","unstructured":"O\u2019Reilly, T., Steele, J., Loukides, M., Hill, C.: Solving the wanamaker problem for healthcare (2012)"},{"issue":"9","key":"736_CR25","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1016\/j.telpol.2015.03.007","volume":"40","author":"D Shin","year":"2016","unstructured":"Shin, D.: Demystifying big data: anatomy of big data developmental process. Telecommun. Policy 40(9), 837\u2013854 (2016)","journal-title":"Telecommun. Policy"},{"key":"736_CR26","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.cosrev.2015.05.002","volume":"17","author":"CK Emani","year":"2015","unstructured":"Emani, C.K., Cullot, N., Nicolle, C.: Understandable big data: a survey. Comput. Sci. Rev. 17, 70\u201381 (2015)","journal-title":"Comput. Sci. Rev."},{"issue":"2","key":"736_CR27","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s11036-013-0489-0","volume":"19","author":"M Chen","year":"2014","unstructured":"Chen, M., Mao, S., Liu, Y.: Big data: A survey. Mob. Netw. Appl. 19(2), 171\u2013209 (2014)","journal-title":"Mob. Netw. Appl."},{"key":"736_CR28","first-page":"3","volume":"2","author":"P Groves","year":"2013","unstructured":"Groves, P., Kayyali, B., Knott, D., Van Kuiken, S.: The \u2018big data\u2019revolution in healthcare. McKinsey Q. 2, 3 (2013)","journal-title":"McKinsey Q."},{"key":"736_CR29","doi-asserted-by":"crossref","unstructured":"Eynon, R.: The rise of big data: what does it mean for education, technology, and media research? (2013)","DOI":"10.1080\/17439884.2013.771783"},{"key":"736_CR30","doi-asserted-by":"crossref","unstructured":"Porche, D.J.: Men\u2019s health big data (2014)","DOI":"10.1177\/1557988314529838"},{"issue":"2","key":"736_CR31","doi-asserted-by":"publisher","first-page":"167","DOI":"10.2217\/cer.14.2","volume":"3","author":"ML Berger","year":"2014","unstructured":"Berger, M.L., Doban, V.: Big data, advanced analytics and the future of comparative effectiveness research. J. Comp. Effect. Res. 3(2), 167\u2013176 (2014)","journal-title":"J. Comp. Effect. Res."},{"key":"736_CR32","unstructured":"BERNARD, E.: Supporting diagnosis and treatment in medical care based on big data processing. In: Cross-Border Challenges in Informatics with a Focus on Disease Surveillance and Utilising Big Data: Proceedings of the EFMI Special Topic Conference, 27-29 April 2014, Budapest, Hungary, vol. 197, p.\u00a065. IOS Press (2014)"},{"key":"736_CR33","doi-asserted-by":"publisher","first-page":"65","DOI":"10.17705\/1CAIS.03465","volume":"34","author":"HJ Watson","year":"2014","unstructured":"Watson, H.J.: Tutorial: Big data analytics: concepts, technologies, and applications. CAIS 34, 65 (2014)","journal-title":"CAIS"},{"issue":"10","key":"736_CR34","first-page":"60","volume":"90","author":"A McAfee","year":"2012","unstructured":"McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D., Barton, D.: Big data: the management revolution. Harv. Bus. Rev. 90(10), 60\u201368 (2012)","journal-title":"Harv. Bus. Rev."},{"issue":"4","key":"736_CR35","first-page":"1","volume":"19","author":"P Russom","year":"2011","unstructured":"Russom, P., et al.: Big data analytics. TDWI Best Pract. Rep. Fourth Quarter 19(4), 1\u201334 (2011)","journal-title":"TDWI Best Pract. Rep. Fourth Quarter"},{"key":"736_CR36","unstructured":"Saporito: The 5 v\u2019s of big data: value and veracity join three more crucial attributes that carriers should consider when developing a big data vision. https:\/\/www.thefreelibrary.com\/The+5+V (2021)"},{"key":"736_CR37","volume-title":"Big Data Analytics: Disruptive Technologies for Changing the Game","author":"A Sathi","year":"2012","unstructured":"Sathi, A.: Big Data Analytics: Disruptive Technologies for Changing the Game. MC Press, Chennai (2012)"},{"issue":"1\u20132","key":"736_CR38","first-page":"118","volume":"10","author":"G Manogaran","year":"2018","unstructured":"Manogaran, G., Lopez, D.: Health data analytics using scalable logistic regression with stochastic gradient descent. Int. J. Adv. Intel. Paradig. 10(1\u20132), 118\u2013132 (2018)","journal-title":"Int. J. Adv. Intel. Paradig."},{"issue":"1","key":"736_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-015-0030-3","volume":"2","author":"CW Tsai","year":"2015","unstructured":"Tsai, C.W., Lai, C.F., Chao, H.C., Vasilakos, A.V.: Big data analytics: a survey. J. Big Data 2(1), 1\u201332 (2015)","journal-title":"J. Big Data"},{"issue":"2","key":"736_CR40","first-page":"118","volume":"31","author":"R James","year":"2014","unstructured":"James, R.: Out of the box: Big data needs the information profession-the importance of validation. Bus. Inf. Rev. 31(2), 118\u2013121 (2014)","journal-title":"Bus. Inf. Rev."},{"issue":"2","key":"736_CR41","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.ijinfomgt.2014.10.007","volume":"35","author":"A Gandomi","year":"2015","unstructured":"Gandomi, A., Haider, M.: Beyond the hype: Big data concepts, methods, and analytics. Int. J. Inf. Manag. 35(2), 137\u2013144 (2015)","journal-title":"Int. J. Inf. Manag."},{"key":"736_CR42","doi-asserted-by":"crossref","unstructured":"Razzak, M.I., Saris, R.A., Blumenstein, M., Xu, G.: Robust 2d joint sparse principal component analysis with f-norm minimization for sparse modelling: 2d-rjspca. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489541"},{"key":"736_CR43","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.neucom.2017.02.081","volume":"243","author":"S Naz","year":"2017","unstructured":"Naz, S., Umar, A.I., Ahmad, R., Siddiqi, I., Ahmed, S.B., Razzak, M.I., Shafait, F.: Urdu nastaliq recognition using convolutional\u2013recursive deep learning. Neurocomputing 243, 80\u201387 (2017)","journal-title":"Neurocomputing"},{"key":"736_CR44","doi-asserted-by":"crossref","unstructured":"Razzak, I., Saris, R.A., Blumenstein, M., Xu, G.: Integrating joint feature selection into subspace learning: A formulation of 2dpca for outliers robust feature selection. Neural Netw. (2019)","DOI":"10.1016\/j.neunet.2019.08.030"},{"key":"736_CR45","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.neucom.2015.11.030","volume":"177","author":"S Naz","year":"2016","unstructured":"Naz, S., Umar, A.I., Ahmad, R., Ahmed, S.B., Shirazi, S.H., Siddiqi, I., Razzak, M.I.: Offline cursive urdu-nastaliq script recognition using multidimensional recurrent neural networks. Neurocomputing 177, 228\u2013241 (2016)","journal-title":"Neurocomputing"},{"key":"736_CR46","unstructured":"Holland, S.M.: Principal components analysis (pca), pp. 30602\u20132501. Department of Geology, University of Georgia, Athens, GA pp (2008)"},{"key":"736_CR47","unstructured":"SVD, S.V.D.: Singular value decomposition. 593\u2013594 (2014)"},{"key":"736_CR48","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Smola, A., M\u00fcller, K.R.: Kernel principal component analysis. In: International Conference on Artificial Neural Networks, pp. 583\u2013588. Springer (1997)","DOI":"10.1007\/BFb0020217"},{"issue":"5","key":"736_CR49","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1109\/T-C.1969.222678","volume":"100","author":"JW Sammon","year":"1969","unstructured":"Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Trans. Comput. 100(5), 401\u2013409 (1969)","journal-title":"IEEE Trans. Comput."},{"issue":"11\u201313","key":"736_CR50","doi-asserted-by":"publisher","first-page":"1307","DOI":"10.1016\/S0167-8655(97)00093-7","volume":"18","author":"D De Ridder","year":"1997","unstructured":"De Ridder, D., Duin, R.P.: Sammon\u2019s mapping using neural networks: a comparison. Pattern Recognit. Lett. 18(11\u201313), 1307\u20131316 (1997)","journal-title":"Pattern Recognit. Lett."},{"issue":"6","key":"736_CR51","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1162\/089976603321780317","volume":"15","author":"M Belkin","year":"2003","unstructured":"Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373\u20131396 (2003)","journal-title":"Neural Comput."},{"key":"736_CR52","doi-asserted-by":"publisher","DOI":"10.3917\/droz.paret.1964.01","volume-title":"Cours d\u2019\u00e9conomie politique","author":"V Pareto","year":"1964","unstructured":"Pareto, V.: Cours d\u2019\u00e9conomie politique, vol. 1. Librairie Droz, Geneva (1964)"},{"key":"736_CR53","unstructured":"Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multiobjective optimization. In: Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on, pp. 82\u201387. Ieee (1994)"},{"key":"736_CR54","volume-title":"Multi-Objective Optimization Using Evolutionary Algorithms","author":"K Deb","year":"2001","unstructured":"Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms, vol. 16. Wiley, Hoboken (2001)"},{"key":"736_CR55","unstructured":"B\u00e4ck, T.: Evolutionary computation: toward a new philosophy of machine intelligence (1997)"},{"key":"736_CR56","volume-title":"Finding Groups in Data: An Introduction to Cluster Analysis","author":"L Kaufman","year":"2009","unstructured":"Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. Wiley, Hoboken (2009)"},{"issue":"2","key":"736_CR57","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/235968.233324","volume":"25","author":"T Zhang","year":"1996","unstructured":"Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. ACM Sigmod Rec. 25(2), 103\u2013114 (1996)","journal-title":"ACM Sigmod Rec."},{"issue":"1","key":"736_CR58","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/1327452.1327492","volume":"51","author":"J Dean","year":"2008","unstructured":"Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107\u2013113 (2008)","journal-title":"Commun. ACM"},{"key":"736_CR59","doi-asserted-by":"crossref","unstructured":"Stanton, I., Kliot, G.: Streaming graph partitioning for large distributed graphs. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1222\u20131230. ACM (2012)","DOI":"10.1145\/2339530.2339722"},{"key":"736_CR60","doi-asserted-by":"crossref","unstructured":"Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1\u201310. IEEE (2010)","DOI":"10.1109\/MSST.2010.5496972"},{"key":"736_CR61","volume-title":"Programming Hive: Data Warehouse and Query Language for Hadoop","author":"E Capriolo","year":"2012","unstructured":"Capriolo, E., Wampler, D., Rutherglen, J.: Programming Hive: Data Warehouse and Query Language for Hadoop. O\u2019Reilly Media Inc, Newton (2012)"},{"key":"736_CR62","unstructured":"Wulff, F.: Presto. https:\/\/prestodb.io\/ (2013)"},{"key":"736_CR63","unstructured":"Hortonworks: Apache mahout. http:\/\/hortonworks.com\/hadoop\/mahout\/ (2015)"},{"key":"736_CR64","unstructured":"Confluent: Avro. http:\/\/docs.confluent.io\/1.0\/avro.html (2015)"},{"key":"736_CR65","doi-asserted-by":"crossref","unstructured":"Razzak, I., Blumenstein, M., Xu, G.: Multiclass support matrix machines by maximizing the inter-class margin for single trial eeg classification. IEEE Trans. Neural Syst. Rehabil. Eng. (2019)","DOI":"10.1109\/TNSRE.2019.2913142"},{"key":"736_CR66","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.ijpe.2014.12.031","volume":"165","author":"SF Wamba","year":"2015","unstructured":"Wamba, S.F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D.: How \u2018big data\u2019can make big impact: findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 165, 234\u2013246 (2015)","journal-title":"Int. J. Prod. Econ."},{"issue":"2","key":"736_CR67","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.ijinfomgt.2016.11.003","volume":"37","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Li, X.: Uses of information and communication technologies in hiv self-management: A systematic review of global literature. Int. J. Inf. Mang. 37(2), 75\u201383 (2017)","journal-title":"Int. J. Inf. Mang."},{"issue":"12","key":"736_CR68","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.1302\/0301-620X.99B12.BJJ-2017-0939","volume":"99","author":"DJ Jacofsky","year":"2017","unstructured":"Jacofsky, D.J.: The myths of \u2018big data\u2019 in health care. Bone Jt J. 99(12), 1571\u20131576 (2017)","journal-title":"Bone Jt J."},{"issue":"1","key":"736_CR69","first-page":"64","volume":"55","author":"Y Wang","year":"2018","unstructured":"Wang, Y., Kung, L., Wang, W.Y.C., Cegielski, C.G.: An integrated big data analytics-enabled transformation model: application to health care. Inf. Mang. 55(1), 64\u201379 (2018)","journal-title":"Inf. Mang."},{"key":"736_CR70","unstructured":"Galetsi, P., Katsaliaki, K.: A review of the literature on big data analytics in healthcare. J. Oper. Res. Soc., 1\u201319 (2019)"},{"issue":"2","key":"736_CR71","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s00034-019-01246-3","volume":"39","author":"A Rehman","year":"2020","unstructured":"Rehman, A., Naz, S., Razzak, M.I., Akram, F., Imran, M.: A deep learning-based framework for automatic brain tumors classification using transfer learning. Circ. Syst. Signal Process. 39(2), 757\u2013775 (2020)","journal-title":"Circ. Syst. Signal Process."},{"key":"736_CR72","unstructured":"Naz, A.R.S., Naseem, U., Razzak, I., Hameed, I.A.: Deep autoencoder-decoder framework for semantic segmentation of brain tumor. Austral. J. Intell. Inf. Process. Syst., 53"},{"key":"736_CR73","doi-asserted-by":"crossref","unstructured":"Rehman, A., Khan, F.G.: A deep learning based review on abdominal images. Multimed. Tools Appl. 1\u201332 (2020)","DOI":"10.1007\/s11042-020-09592-0"},{"issue":"3","key":"736_CR74","doi-asserted-by":"publisher","first-page":"335","DOI":"10.3233\/THC-161133","volume":"24","author":"SH Shirazi","year":"2016","unstructured":"Shirazi, S.H., Umar, A.I., Naz, S., Razzak, M.I.: Efficient leukocyte segmentation and recognition in peripheral blood image. Technol. Health Care 24(3), 335\u2013347 (2016)","journal-title":"Technol. Health Care"},{"key":"736_CR75","doi-asserted-by":"crossref","unstructured":"Razzak, I., Imran, M., Xu, G.: Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE J. Biomed. Health Inf. (2018)","DOI":"10.1109\/JBHI.2018.2874033"},{"issue":"2","key":"736_CR76","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s00521-015-2051-4","volume":"28","author":"S Naz","year":"2017","unstructured":"Naz, S., Umar, A.I., Ahmad, R., Ahmed, S.B., Shirazi, S.H., Razzak, M.I.: Urdu nasta\u2019liq text recognition system based on multi-dimensional recurrent neural network and statistical features. Neural Comput. Appl. 28(2), 219\u2013231 (2017)","journal-title":"Neural Comput. Appl."},{"key":"736_CR77","first-page":"14","volume":"2013","author":"RC Gessner","year":"2013","unstructured":"Gessner, R.C., Frederick, C.B., Foster, F.S., Dayton, P.A.: Acoustic angiography: a new imaging modality for assessing microvasculature architecture. J. Biomed. Imaging 2013, 14 (2013)","journal-title":"J. Biomed. Imaging"},{"key":"736_CR78","unstructured":"Shackelford, K.: System & method for delineation and quantification of fluid accumulation in efast trauma ultrasound images. US Patent App. 14\/167,448 (2014)"},{"key":"736_CR79","doi-asserted-by":"crossref","unstructured":"Chen, W., Cockrell, C., Ward, K.R., Najarian, K.: Intracranial pressure level prediction in traumatic brain injury by extracting features from multiple sources and using machine learning methods. In: Bioinformatics and Biomedicine (BIBM), 2010 IEEE International Conference on, pp. 510\u2013515. IEEE (2010)","DOI":"10.1109\/BIBM.2010.5706619"},{"issue":"2","key":"736_CR80","first-page":"216","volume":"9","author":"QA Yao","year":"2014","unstructured":"Yao, Q.A., Zheng, H., Xu, Z.Y., Wu, Q., Li, Z.W., Yun, L.: Massive medical images retrieval system based on hadoop. J. Multimed. 9(2), 216\u2013222 (2014)","journal-title":"J. Multimed."},{"key":"736_CR81","doi-asserted-by":"crossref","unstructured":"Jai-Andaloussi, S., Elabdouli, A., Chaffai, A., Madrane, N., Sekkaki, A.: Medical content based image retrieval by using the hadoop framework. In: Telecommunications (ICT), 2013 20th International Conference on, pp. 1\u20135. IEEE (2013)","DOI":"10.1109\/ICTEL.2013.6632112"},{"issue":"1","key":"736_CR82","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s11886-013-0441-8","volume":"16","author":"SE Dilsizian","year":"2014","unstructured":"Dilsizian, S.E., Siegel, E.L.: Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr. Cardiol. Rep. 16(1), 441 (2014)","journal-title":"Curr. Cardiol. Rep."},{"key":"736_CR83","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.jbi.2015.12.005","volume":"59","author":"S Istephan","year":"2016","unstructured":"Istephan, S., Siadat, M.R.: Unstructured medical image query using big data\u2014an epilepsy case study. J. Biomed. Inf. 59, 218\u2013226 (2016)","journal-title":"J. Biomed. Inf."},{"issue":"5","key":"736_CR84","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.jbi.2013.07.001","volume":"46","author":"A O\u2019Driscoll","year":"2013","unstructured":"O\u2019Driscoll, A., Daugelaite, J., Sleator, R.D.: \u2018big data\u2019, hadoop and cloud computing in genomics. J. Biomed. Inf. 46(5), 774\u2013781 (2013)","journal-title":"J. Biomed. Inf."},{"key":"736_CR85","unstructured":"Robison, R.J.: https:\/\/medium.com\/precision-medicine\/how-big-is-the-human-genome-e90caa3409b0 (2014)"},{"issue":"9","key":"736_CR86","first-page":"837","volume":"13","author":"H Kashya","year":"2014","unstructured":"Kashya, H., Ahmed, H.A., Hoque, N., Roy, S., Bhattacharyya, D.K.: Big data analytics in bioinformatics: a machine learning perspective. J. Latex Class Files 13(9), 837\u2013854 (2014)","journal-title":"J. Latex Class Files"},{"key":"736_CR87","unstructured":"Lander\u00a0Eric, S., Linton\u00a0Lauren, M., Bruce, B., Chad, N., Zody\u00a0Michael, C., Jennifer, B., Keri, D., Ken, D., Michael, D., William, F., et\u00a0al.: Initial sequencing and analysis of the human genome. (2001)"},{"issue":"5961","key":"736_CR88","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1126\/science.1181498","volume":"327","author":"R Drmanac","year":"2010","unstructured":"Drmanac, R., Sparks, A.B., Callow, M.J., Halpern, A.L., Burns, N.L., Kermani, B.G., Carnevali, P., Nazarenko, I., Nilsen, G.B., Yeung, G., et al.: Human genome sequencing using unchained base reads on self-assembling dna nanoarrays. Science 327(5961), 78\u201381 (2010)","journal-title":"Science"},{"issue":"4","key":"736_CR89","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.2307\/41703503","volume":"36","author":"H Chen","year":"2012","unstructured":"Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165\u20131188 (2012)","journal-title":"MIS Q"},{"issue":"4","key":"736_CR90","first-page":"5865","volume":"5","author":"K Priyanka","year":"2014","unstructured":"Priyanka, K., Kulennavar, N.: A survey on big data analytics in health care. Int. J. Comput. Sci. Inf. Technol. 5(4), 5865\u20135868 (2014)","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"issue":"1","key":"736_CR91","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.cell.2013.09.006","volume":"155","author":"DC Koboldt","year":"2013","unstructured":"Koboldt, D.C., Steinberg, K.M., Larson, D.E., Wilson, R.K., Mardis, E.R.: The next-generation sequencing revolution and its impact on genomics. Cell 155(1), 27\u201338 (2013)","journal-title":"Cell"},{"key":"736_CR92","doi-asserted-by":"crossref","unstructured":"Kanz, C., Aldebert, P., Althorpe, N., Baker, W., Baldwin, A., Bates, K., Browne, P., van\u00a0den Broek, A., Castro, M., Cochrane, G., et\u00a0al.: The embl nucleotide sequence database. Nucl. Acids Res. 33(suppl\\_1), D29\u2013D33 (2005)","DOI":"10.1093\/nar\/gki098"},{"issue":"5","key":"736_CR93","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.1093\/nar\/16.5.1861","volume":"16","author":"HS Bilofsky","year":"1988","unstructured":"Bilofsky, H.S., Christian, B.: The genbank\u00ae genetic sequence data bank. Nucl. Acids Res. 16(5), 1861\u20131863 (1988)","journal-title":"Nucl. Acids Res."},{"issue":"6","key":"736_CR94","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1016\/j.ajhg.2009.10.023","volume":"85","author":"YG Yao","year":"2009","unstructured":"Yao, Y.G., Salas, A., Logan, I., Bandelt, H.J.: mtdna data mining in genbank needs surveying. Am. J. Hum. Genet. 85(6), 929\u2013933 (2009)","journal-title":"Am. J. Hum. Genet."},{"key":"736_CR95","doi-asserted-by":"crossref","unstructured":"Sugawara, H., Ogasawara, O., Okubo, K., Gojobori, T., Tateno, Y.: DDBJ with new system and face. Nucl. Acids Res. 36(suppl_1), D22\u2013D24 (2007)","DOI":"10.1093\/nar\/gkm889"},{"issue":"1","key":"736_CR96","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1093\/nar\/26.1.94","volume":"26","author":"SI Letovsky","year":"1998","unstructured":"Letovsky, S.I., Cottingham, R.W., Porter, C.J., Li, P.W.: Gdb: the human genome database. Nucl. Acids Res. 26(1), 94\u201399 (1998)","journal-title":"Nucl. Acids Res."},{"issue":"10\u201311","key":"736_CR97","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1016\/j.crvi.2005.06.001","volume":"328","author":"B Boeckmann","year":"2005","unstructured":"Boeckmann, B., Blatter, M.C., Famiglietti, L., Hinz, U., Lane, L., Roechert, B., Bairoch, A.: Protein variety and functional diversity: Swiss-prot annotation in its biological context. Compt. rendus Biol. 328(10\u201311), 882\u2013899 (2005)","journal-title":"Compt. rendus Biol."},{"issue":"1","key":"736_CR98","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1093\/nar\/gkg095","volume":"31","author":"B Boeckmann","year":"2003","unstructured":"Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.C., Estreicher, A., Gasteiger, E., Martin, M.J., Michoud, K., O\u2019donovan, C., Phan, I., et al.: The swiss-prot protein knowledgebase and its supplement trembl in 2003. Nucl. Acids Res. 31(1), 365\u2013370 (2003)","journal-title":"Nucl. Acids Res."},{"key":"736_CR99","unstructured":"GDB: http:\/\/www.bioinfo.pte.hu\/more\/TrEMBL.htm. Accessed 15 Mar 2018"},{"key":"736_CR100","doi-asserted-by":"crossref","unstructured":"Hulo, N., Bairoch, A., Bulliard, V., Cerutti, L., De\u00a0Castro, E., Langendijk-Genevaux, P.S., Pagni, M., Sigrist, C.J.: The prosite database. Nucl. Acids Res. 34(suppl\\_1), D227\u2013D230 (2006)","DOI":"10.1093\/nar\/gkj063"},{"key":"736_CR101","doi-asserted-by":"crossref","unstructured":"Kouranov, A., Xie, L., de\u00a0la Cruz, J., Chen, L., Westbrook, J., Bourne, P.E., Berman, H.M.: The rcsb pdb information portal for structural genomics. Nucl. Acids Res. 34(suppl\\_1), D302\u2013D305 (2006)","DOI":"10.1093\/nar\/gkj120"},{"issue":"1","key":"736_CR102","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1186\/1471-2105-7-170","volume":"7","author":"TJ Lee","year":"2006","unstructured":"Lee, T.J., Pouliot, Y., Wagner, V., Gupta, P., Stringer-Calvert, D.W., Tenenbaum, J.D., Karp, P.D.: Biowarehouse: a bioinformatics database warehouse toolkit. BMC Bioinform. 7(1), 170 (2006)","journal-title":"BMC Bioinform."},{"key":"736_CR103","doi-asserted-by":"crossref","unstructured":"Bagyamathi, M., Inbarani, H.H.: A novel hybridized rough set and improved harmony search based feature selection for protein sequence classification. In: Big Data in Complex Systems, pp. 173\u2013204. Springer (2015)","DOI":"10.1007\/978-3-319-11056-1_6"},{"key":"736_CR104","unstructured":"Barbu, A., She, Y., Ding, L., Gramajo, G.: Feature selection with annealing for big data learning. arXiv preprint (2013)"},{"key":"736_CR105","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.fss.2014.08.014","volume":"258","author":"A Zeng","year":"2015","unstructured":"Zeng, A., Li, T., Liu, D., Zhang, J., Chen, H.: A fuzzy rough set approach for incremental feature selection on hybrid information systems. Fuzzy Sets Syst. 258, 39\u201360 (2015)","journal-title":"Fuzzy Sets Syst."},{"key":"736_CR106","unstructured":"Mitchell, T.M., et\u00a0al.: Machine learning. 1997. Burr Ridge, IL: McGraw Hill 45(37), 870\u2013877 (1997)"},{"key":"736_CR107","volume-title":"Pattern Classification","author":"RO Duda","year":"1973","unstructured":"Duda, R.O., Hart, P.E., Stork, D.G., et al.: Pattern Classification, vol. 2. Wiley, New York (1973)"},{"key":"736_CR108","volume-title":"Pattern Recognition and Machine Learning","author":"Y Anzai","year":"2012","unstructured":"Anzai, Y.: Pattern Recognition and Machine Learning. Elsevier, Amsterdam (2012)"},{"key":"736_CR109","volume-title":"Foundations of Machine Learning","author":"M Mohri","year":"2012","unstructured":"Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. MIT Press, Cambridge (2012)"},{"key":"736_CR110","unstructured":"Hsieh, C.J., Si, S., Dhillon, I.: A divide-and-conquer solver for kernel support vector machines. In: International Conference on Machine Learning, pp. 566\u2013574 (2014)"},{"key":"736_CR111","volume-title":"Big Data Algorithms for Visualization and Supervised Learning","author":"N Djuric","year":"2013","unstructured":"Djuric, N.: Big Data Algorithms for Visualization and Supervised Learning. Temple University, Philadelphia (2013)"},{"key":"736_CR112","doi-asserted-by":"crossref","unstructured":"Giveki, D., Salimi, H., Bahmanyar, G., Khademian, Y.: Automatic detection of diabetes diagnosis using feature weighted support vector machines based on mutual information and modified cuckoo search. arXiv preprint arXiv:1201.2173 (2012)","DOI":"10.5120\/9371-9528"},{"issue":"11","key":"736_CR113","doi-asserted-by":"publisher","first-page":"2123","DOI":"10.3174\/ajnr.A3126","volume":"33","author":"S Haller","year":"2012","unstructured":"Haller, S., Badoud, S., Nguyen, D., Garibotto, V., Lovblad, K., Burkhard, P.: Individual detection of patients with parkinson disease using support vector machine analysis of diffusion tensor imaging data: initial results. Am. J. Neuroradiol. 33(11), 2123\u20132128 (2012)","journal-title":"Am. J. Neuroradiol."},{"issue":"4","key":"736_CR114","doi-asserted-by":"publisher","first-page":"253","DOI":"10.4258\/hir.2010.16.4.253","volume":"16","author":"YJ Son","year":"2010","unstructured":"Son, Y.J., Kim, H.G., Kim, E.H., Choi, S., Lee, S.K.: Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc. Inf. Res. 16(4), 253\u2013259 (2010)","journal-title":"Healthc. Inf. Res."},{"key":"736_CR115","unstructured":"Bhatia, S., Prakash, P., Pillai, G.: Svm based decision support system for heart disease classification with integer-coded genetic algorithm to select critical features. In: Proceedings of the World Congress on Engineering and Computer Science, pp. 34\u201338 (2008)"},{"key":"736_CR116","doi-asserted-by":"crossref","unstructured":"Ye, J., Chow, J.H., Chen, J., Zheng, Z.: Stochastic gradient boosted distributed decision trees. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 2061\u20132064. ACM (2009)","DOI":"10.1145\/1645953.1646301"},{"key":"736_CR117","unstructured":"Calaway, R., Edlefsen, L., Gong, L., Fast, S.: Big data decision trees with r. Revolution (2016)"},{"key":"736_CR118","doi-asserted-by":"crossref","unstructured":"Hall, L.O., Chawla, N., Bowyer, K.W.: Decision tree learning on very large data sets. In: Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on, vol.\u00a03, pp. 2579\u20132584. IEEE (1998)","DOI":"10.1109\/ICSMC.1998.725047"},{"issue":"5","key":"736_CR119","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1109\/TKDE.2002.1033770","volume":"14","author":"RT Ng","year":"2002","unstructured":"Ng, R.T., Han, J.: Clarans: a method for clustering objects for spatial data mining. IEEE Trans. Knowl. Data Eng. 14(5), 1003\u20131016 (2002)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"34","key":"736_CR120","first-page":"226","volume":"96","author":"M Ester","year":"1996","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96(34), 226\u2013231 (1996)","journal-title":"Kdd"},{"key":"736_CR121","first-page":"58","volume":"98","author":"A Hinneburg","year":"1998","unstructured":"Hinneburg, A., Keim, D.A., et al.: An efficient approach to clustering in large multimedia databases with noise. KDD 98, 58\u201365 (1998)","journal-title":"KDD"},{"issue":"2","key":"736_CR122","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1145\/276305.276312","volume":"27","author":"S Guha","year":"1998","unstructured":"Guha, S., Rastogi, R., Shim, K.: CURE: an efficient clustering algorithm for large databases. ACM Sigmod Rec. 27(2), 73\u201384 (1998)","journal-title":"ACM Sigmod Rec"},{"issue":"3","key":"736_CR123","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1023\/A:1009769707641","volume":"2","author":"Z Huang","year":"1998","unstructured":"Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discov. 2(3), 283\u2013304 (1998)","journal-title":"Data Min. Knowl. Discov."},{"key":"736_CR124","doi-asserted-by":"crossref","unstructured":"Xu, X., J\u00e4ger, J., Kriegel, H.P.: A fast parallel clustering algorithm for large spatial databases. In: High Performance Data Mining, pp. 263\u2013290. Springer (1999)","DOI":"10.1007\/0-306-47011-X_3"},{"issue":"1","key":"736_CR125","first-page":"1","volume":"13","author":"N Chen","year":"2002","unstructured":"Chen, N., Chen, A.Z., Zhou, L.X.: An incremental grid density-based clustering algorithm. J. Softw. 13(1), 1\u20137 (2002)","journal-title":"J. Softw."},{"key":"736_CR126","unstructured":"Kumar, V., Sharma, R.M., Thakur, R.: Big data analytics: Bioinformatics perspective. (2016)"},{"issue":"6","key":"736_CR127","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1007\/s10439-007-9313-y","volume":"35","author":"TH Stokes","year":"2007","unstructured":"Stokes, T.H., Moffitt, R.A., Phan, J.H., Wang, M.D.: chip artifact correction (cacorrect): a bioinformatics system for quality assurance of genomics and proteomics array data. Ann. Biomed. Eng. 35(6), 1068\u20131080 (2007)","journal-title":"Ann. Biomed. Eng."},{"issue":"12","key":"736_CR128","doi-asserted-by":"publisher","first-page":"3364","DOI":"10.1109\/TBME.2012.2212438","volume":"60","author":"JH Phan","year":"2013","unstructured":"Phan, J.H., Young, A.N., Wang, M.D.: omnibiomarker: a web-based application for knowledge-driven biomarker identification. IEEE Trans. Biomed. Eng. 60(12), 3364\u20133367 (2013)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"736_CR129","doi-asserted-by":"publisher","first-page":"e0116776","DOI":"10.1371\/journal.pone.0116776","volume":"10","author":"M Liang","year":"2015","unstructured":"Liang, M., Zhang, F., Jin, G., Zhu, J.: FastGCN: a GPU accelerated tool for fast gene co-expression networks. PloS one 10(1), e0116776 (2015)","journal-title":"PloS one"},{"issue":"12","key":"736_CR130","doi-asserted-by":"publisher","first-page":"e8491","DOI":"10.1371\/journal.pone.0008491","volume":"4","author":"A Day","year":"2009","unstructured":"Day, A., Dong, J., Funari, V.A., Harry, B., Strom, S.P., Cohn, D.H., Nelson, S.F.: Disease gene characterization through large-scale co-expression analysis. PLoS one 4(12), e8491 (2009)","journal-title":"PLoS one"},{"issue":"1","key":"736_CR131","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1186\/1471-2105-9-559","volume":"9","author":"P Langfelder","year":"2008","unstructured":"Langfelder, P., Horvath, S.: Wgcna: an r package for weighted correlation network analysis. BMC Bioinform. 9(1), 559 (2008)","journal-title":"BMC Bioinform."},{"issue":"1","key":"736_CR132","doi-asserted-by":"publisher","first-page":"S61","DOI":"10.1186\/1471-2105-11-S1-S61","volume":"11","author":"CG Rivera","year":"2010","unstructured":"Rivera, C.G., Vakil, R., Bader, J.S.: Nemo: network module identification in cytoscape. BMC Bioinform. 11(1), S61 (2010)","journal-title":"BMC Bioinform."},{"issue":"1","key":"736_CR133","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/1471-2105-4-2","volume":"4","author":"GD Bader","year":"2003","unstructured":"Bader, G.D., Hogue, C.W.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 4(1), 2 (2003)","journal-title":"BMC Bioinform."},{"issue":"5","key":"736_CR134","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1038\/nmeth.1938","volume":"9","author":"T Nepusz","year":"2012","unstructured":"Nepusz, T., Yu, H., Paccanaro, A.: Detecting overlapping protein complexes in protein-protein interaction networks. Nat. Methods 9(5), 471 (2012)","journal-title":"Nat. Methods"},{"key":"736_CR135","doi-asserted-by":"crossref","unstructured":"Kelley, B.P., Yuan, B., Lewitter, F., Sharan, R., Stockwell, B.R., Ideker, T.: Pathblast: a tool for alignment of protein interaction networks. Nucl. Acids Res. 32(suppl\\_2), W83\u2013W88 (2004)","DOI":"10.1093\/nar\/gkh411"},{"issue":"16","key":"736_CR136","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.1093\/bioinformatics\/bts366","volume":"28","author":"AC Zambon","year":"2012","unstructured":"Zambon, A.C., Gaj, S., Ho, I., Hanspers, K., Vranizan, K., Evelo, C.T., Conklin, B.R., Pico, A.R., Salomonis, N.: Go-elite: a flexible solution for pathway and ontology over-representation. Bioinformatics 28(16), 2209\u20132210 (2012)","journal-title":"Bioinformatics"},{"issue":"1","key":"736_CR137","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-9-399","volume":"9","author":"MP van Iersel","year":"2008","unstructured":"van Iersel, M.P., Kelder, T., Pico, A.R., Hanspers, K., Coort, S., Conklin, B.R., Evelo, C.: Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics 9(1), 1\u20139 (2008)","journal-title":"BMC Bioinformatics"},{"issue":"6","key":"736_CR138","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1093\/bioinformatics\/btt616","volume":"30","author":"P Yang","year":"2013","unstructured":"Yang, P., Patrick, E., Tan, S.X., Fazakerley, D.J., Burchfield, J., Gribben, C., Prior, M.J., James, D.E., Hwa Yang, Y.: Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway. Bioinformatics 30(6), 808\u2013814 (2013)","journal-title":"Bioinformatics"},{"issue":"7","key":"736_CR139","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1101\/gr.226602","volume":"12","author":"P Grosu","year":"2002","unstructured":"Grosu, P., Townsend, J.P., Hartl, D.L., Cavalieri, D.: Pathway processor: a tool for integrating whole-genome expression results into metabolic networks. Genome Res. 12(7), 1121\u20131126 (2002)","journal-title":"Genome Res."},{"issue":"1","key":"736_CR140","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1186\/1471-2105-14-267","volume":"14","author":"YS Park","year":"2013","unstructured":"Park, Y.S., Schmidt, M., Martin, E.R., Pericak-Vance, M.A., Chung, R.H.: Pathway-pdt: a flexible pathway analysis tool for nuclear families. BMC Bioinform. 14(1), 267 (2013)","journal-title":"BMC Bioinform."},{"issue":"14","key":"736_CR141","doi-asserted-by":"publisher","first-page":"1830","DOI":"10.1093\/bioinformatics\/btt285","volume":"29","author":"W Luo","year":"2013","unstructured":"Luo, W., Brouwer, C.: Pathview: an r\/bioconductor package for pathway-based data integration and visualization. Bioinformatics 29(14), 1830\u20131831 (2013)","journal-title":"Bioinformatics"},{"issue":"11","key":"736_CR142","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1093\/bioinformatics\/btp236","volume":"25","author":"MC Schatz","year":"2009","unstructured":"Schatz, M.C.: Cloudburst: highly sensitive read mapping with mapreduce. Bioinformatics 25(11), 1363\u20131369 (2009)","journal-title":"Bioinformatics"},{"key":"736_CR143","unstructured":"Schatz, M., Sommer, D., Kelley, D., Pop, M.: Contrail: Assembly of large genomes using cloud computing. In: CSHL Biology of Genomes Conference (2010)"},{"key":"736_CR144","doi-asserted-by":"crossref","unstructured":"Gurtowski, J., Schatz, M.C., Langmead, B.: Genotyping in the cloud with crossbow. Curr. Protoc. Bioinform., 3\u201315 (2012)","DOI":"10.1002\/0471250953.bi1503s39"},{"issue":"1","key":"736_CR145","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1186\/1471-2105-13-324","volume":"13","author":"S Lewis","year":"2012","unstructured":"Lewis, S., Csordas, A., Killcoyne, S., Hermjakob, H., Hoopmann, M.R., Moritz, R.L., Deutsch, E.W., Boyle, J.: Hydra: a scalable proteomic search engine which utilizes the hadoop distributed computing framework. BMC Bioinform. 13(1), 324 (2012)","journal-title":"BMC Bioinform."},{"issue":"12","key":"736_CR146","first-page":"S2","volume":"11","author":"DB O\u2019Connor","year":"2010","unstructured":"O\u2019Connor, D.B., Merriman, B., Nelson, S.F.: Seqware query engine: storing and searching sequence data in the cloud. BMC Inform 11(12), S2 (2010)","journal-title":"BMC Inform"},{"key":"736_CR147","volume-title":"HBase: The Definitive Guide: Random Access to Your Planet-Size Data","author":"L George","year":"2011","unstructured":"George, L.: HBase: The Definitive Guide: Random Access to Your Planet-Size Data. O\u2019Reilly Media, Inc., Newton (2011)"},{"issue":"1","key":"736_CR148","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2164-12-419","volume":"12","author":"T Robinson","year":"2011","unstructured":"Robinson, T., Killcoyne, S., Bressler, R., Boyle, J.: SAMQA: error classification and validation of high-throughput sequenced read data. BMC Genom. 12(1), 1\u20137 (2011)","journal-title":"BMC Genom."},{"issue":"4","key":"736_CR149","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1093\/bioinformatics\/btr708","volume":"28","author":"W Huang","year":"2011","unstructured":"Huang, W., Li, L., Myers, J.R., Marth, G.T.: Art: a next-generation sequencing read simulator. Bioinformatics 28(4), 593\u2013594 (2011)","journal-title":"Bioinformatics"},{"key":"736_CR150","doi-asserted-by":"crossref","unstructured":"Chen, C.C., Chang, Y.J., Chung, W.C., Lee, D.T., Ho, J.M.: Cloudrs: an error correction algorithm of high-throughput sequencing data based on scalable framework. In: Big Data, 2013 IEEE International Conference on, pp. 717\u2013722. IEEE (2013)","DOI":"10.1109\/BigData.2013.6691642"},{"issue":"4","key":"736_CR151","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1073\/pnas.1017351108","volume":"108","author":"S Gnerre","year":"2011","unstructured":"Gnerre, S., MacCallum, I., Przybylski, D., Ribeiro, F.J., Burton, J.N., Walker, B.J., Sharpe, T., Hall, G., Shea, T.P., Sykes, S., et al.: High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proc. Natl. Acad. Sci. 108(4), 1513\u20131518 (2011)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"1","key":"736_CR152","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1186\/1471-2105-12-356","volume":"12","author":"SV Angiuoli","year":"2011","unstructured":"Angiuoli, S.V., Matalka, M., Gussman, A., Galens, K., Vangala, M., Riley, D.R., Arze, C., White, J.R., White, O., Fricke, W.F.: Clovr: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing. BMC Bioinformatics 12(1), 356 (2011)","journal-title":"BMC Bioinformatics"},{"key":"736_CR153","unstructured":"Eelmets, M.: Clovr: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing. \u2013 -(-) (2011)"},{"issue":"1","key":"736_CR154","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/1471-2105-13-42","volume":"13","author":"K Krampis","year":"2012","unstructured":"Krampis, K., Booth, T., Chapman, B., Tiwari, B., Bicak, M., Field, D., Nelson, K.E.: Cloud biolinux: pre-configured and on-demand bioinformatics computing for the genomics community. BMC Bioinf. 13(1), 42 (2012)","journal-title":"BMC Bioinf."},{"issue":"9","key":"736_CR155","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1101\/gr.107524.110","volume":"20","author":"A McKenna","year":"2010","unstructured":"McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al.: The genome analysis toolkit: a mapreduce framework for analyzing next-generation dna sequencing data. Genome Res. 20(9), 1297\u20131303 (2010)","journal-title":"Genome Res."},{"key":"736_CR156","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1002\/0471250953.bi1110s43","volume":"43","author":"GA Van der Auwera","year":"2013","unstructured":"Van der Auwera, G.A., Carneiro, M.O., Hartl, C., Poplin, R., Del Angel, G., Levy-Moonshine, A., Jordan, T., Shakir, K., Roazen, D., Thibault, J., et al.: From fastq data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinform. 43, 10\u201311 (2013)","journal-title":"Curr. Protoc. Bioinform."},{"issue":"1","key":"736_CR157","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1093\/bioinformatics\/bts647","volume":"29","author":"H Huang","year":"2012","unstructured":"Huang, H., Tata, S., Prill, R.J.: Bluesnp: R package for highly scalable genome-wide association studies using hadoop clusters. Bioinformatics 29(1), 135\u2013136 (2012)","journal-title":"Bioinformatics"},{"issue":"5","key":"736_CR158","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.outlook.2008.06.009","volume":"56","author":"PA Abbott","year":"2008","unstructured":"Abbott, P.A., Coenen, A.: Globalization and advances in information and communication technologies: the impact on nursing and health. Nurs Outlook 56(5), 238\u2013246 (2008)","journal-title":"Nurs Outlook"},{"issue":"6","key":"736_CR159","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1057\/palgrave.ejis.3000717","volume":"16","author":"A Bhattacherjee","year":"2007","unstructured":"Bhattacherjee, A., Hikmet, N.: Physicians\u2019 resistance toward healthcare information technology: a theoretical model and empirical test. Eur. J. Inf. Syst. 16(6), 725\u2013737 (2007)","journal-title":"Eur. J. Inf. Syst."},{"issue":"5","key":"736_CR160","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1056\/NEJMp0912825","volume":"362","author":"D Blumenthal","year":"2010","unstructured":"Blumenthal, D.: Launching hitech. N. Engl. J. Med. 362(5), 382\u2013385 (2010)","journal-title":"N. Engl. J. Med."},{"key":"736_CR161","doi-asserted-by":"crossref","unstructured":"Bakshi, K.: Considerations for big data: architecture and approach. In: Aerospace Conference, 2012 IEEE, pp. 1\u20137. IEEE (2012)","DOI":"10.1109\/AERO.2012.6187357"},{"key":"736_CR162","first-page":"261","volume":"11","author":"H Herodotou","year":"2011","unstructured":"Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F.B., Babu, S.: Starfish: a self-tuning system for big data analytics. Cidr 11, 261\u2013272 (2011)","journal-title":"Cidr"},{"key":"736_CR163","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1377\/hlthaff.2011.0178","volume":"30","author":"MB Buntin","year":"2011","unstructured":"Buntin, M.B., Burke, M.F., Hoaglin, M.C., Blumenthal, D.: The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Affairs 30, 464\u2013471 (2011)","journal-title":"Health Affairs"},{"key":"736_CR164","doi-asserted-by":"crossref","unstructured":"Dutta, H., Kamil, A., Pooleery, M., Sethumadhavan, S., Demme, J.: Distributed storage of large-scale multidimensional electroencephalogram data using hadoop and hbase. In: Grid and Cloud Database Management, pp. 331\u2013347. Springer (2011)","DOI":"10.1007\/978-3-642-20045-8_16"},{"key":"736_CR165","doi-asserted-by":"crossref","unstructured":"Jin, Y., Deyu, T., Yi, Z.: A distributed storage model for ehr based on hbase. In: Information Management, Innovation Management and Industrial Engineering (ICIII), 2011 International Conference on, vol.\u00a02, pp. 369\u2013372. IEEE (2011)","DOI":"10.1109\/ICIII.2011.234"},{"key":"736_CR166","unstructured":"Nguyen, A.V., Wynden, R., Sun, Y.: Hbase, mapreduce, and integrated data visualization for processing clinical signal data. In: AAAI Spring Symposium: Computational Physiology, vol. 2011. California, CA: Association for the Advancement of Artificial Intelligence (2011)"},{"key":"736_CR167","unstructured":"Jayapandian, C.P., Chen, C.H., Bozorgi, A., Lhatoo, S.D., Zhang, G.Q., Sahoo, S.S.: Cloudwave: distributed processing of \u201cbig data\u201d from electrophysiological recordings for epilepsy clinical research using hadoop. In: AMIA Annual Symposium Proceedings, vol. 2013, p. 691. American Medical Informatics Association (2013)"},{"issue":"2","key":"736_CR168","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1136\/amiajnl-2013-002156","volume":"21","author":"SS Sahoo","year":"2013","unstructured":"Sahoo, S.S., Jayapandian, C., Garg, G., Kaffashi, F., Chung, S., Bozorgi, A., Chen, C.H., Loparo, K., Lhatoo, S.D., Zhang, G.Q.: Heart beats in the cloud: distributed analysis of electrophysiological \u2018big data\u2019using cloud computing for epilepsy clinical research. J. Am. Med. Inf. Assoc. 21(2), 263\u2013271 (2013)","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"736_CR169","doi-asserted-by":"crossref","unstructured":"Mazurek, M.: Applying nosql databases for operationalizing clinical data mining models. In: International Conference: Beyond Databases, Architectures and Structures, pp. 527\u2013536. Springer (2014)","DOI":"10.1007\/978-3-319-06932-6_51"},{"issue":"5","key":"736_CR170","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1109\/JBHI.2013.2257818","volume":"17","author":"A Bahga","year":"2013","unstructured":"Bahga, A., Madisetti, V.K.: A cloud-based approach for interoperable electronic health records (ehrs). IEEE J. Biomed. Health Inf. 17(5), 894\u2013906 (2013)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"736_CR171","doi-asserted-by":"crossref","unstructured":"Chen, J., Qian, F., Yan, W., Shen, B.: Translational biomedical informatics in the cloud: present and future. BioMed Res. Int., (2013)","DOI":"10.1155\/2013\/658925"},{"key":"736_CR172","doi-asserted-by":"crossref","unstructured":"Sharp, J.: An application architecture to facilitate multi-site clinical trial collaboration in the cloud. In: Proceedings of the 2nd International Workshop on Software Engineering for Cloud Computing, pp. 64\u201368. ACM (2011)","DOI":"10.1145\/1985500.1985511"},{"key":"736_CR173","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.jbi.2013.12.012","volume":"48","author":"K Ng","year":"2014","unstructured":"Ng, K., Ghoting, A., Steinhubl, S.R., Stewart, W.F., Malin, B., Sun, J.: Paramo: a parallel predictive modeling platform for healthcare analytic research using electronic health records. J. Biomed. Inf. 48, 160\u2013170 (2014)","journal-title":"J. Biomed. Inf."},{"issue":"3","key":"736_CR174","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1007\/s11606-013-2455-8","volume":"28","author":"NV Chawla","year":"2013","unstructured":"Chawla, N.V., Davis, D.A.: Bringing big data to personalized healthcare: a patient-centered framework. J. Gener. Intern. Med. 28(3), 660\u2013665 (2013)","journal-title":"J. Gener. Intern. Med."},{"key":"736_CR175","first-page":"225","volume":"99","author":"R Abbott","year":"2013","unstructured":"Abbott, R.: Big data and pharmacovigilance: using health information exchanges to revolutionize drug safety. Iowa L. Rev. 99, 225 (2013)","journal-title":"Iowa L. Rev."},{"key":"736_CR176","doi-asserted-by":"crossref","unstructured":"Zolfaghar, K., Meadem, N., Teredesai, A., Roy, S.B., Chin, S.C., Muckian, B.: Big data solutions for predicting risk-of-readmission for congestive heart failure patients. In: Big Data, 2013 IEEE International Conference on, pp. 64\u201371. IEEE (2013)","DOI":"10.1109\/BigData.2013.6691760"},{"key":"736_CR177","doi-asserted-by":"crossref","unstructured":"Rangarajan, S., Liu, H., Wang, H., Wang, C.L.: Scalable architecture for personalized healthcare service recommendation using big data lake. In: Service Research and Innovation, pp. 65\u201379. Springer (2015)","DOI":"10.1007\/978-3-319-76587-7_5"},{"key":"736_CR178","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.jbusres.2016.08.002","volume":"70","author":"Y Wang","year":"2017","unstructured":"Wang, Y., Hajli, N.: Exploring the path to big data analytics success in healthcare. J. Bus. Res. 70, 287\u2013299 (2017)","journal-title":"J. Bus. Res."},{"issue":"5","key":"736_CR179","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1097\/CCM.0b013e31820a92c6","volume":"39","author":"M Saeed","year":"2011","unstructured":"Saeed, M., Villarroel, M., Reisner, A.T., Clifford, G., Lehman, L.W., Moody, G., Heldt, T., Kyaw, T.H., Moody, B., Mark, R.G.: Multiparameter intelligent monitoring in intensive care ii (mimic-ii): a public-access intensive care unit database. Crit. Care Med. 39(5), 952 (2011)","journal-title":"Crit. Care Med."},{"issue":"12","key":"736_CR180","first-page":"1117","volume":"8","author":"BF Hankey","year":"1999","unstructured":"Hankey, B.F., Ries, L.A., Edwards, B.K.: The surveillance, epidemiology, and end results program: a national resource. Cancer Epidemiol. Prev. Biomark. 8(12), 1117\u20131121 (1999)","journal-title":"Cancer Epidemiol. Prev. Biomark."},{"issue":"11","key":"736_CR181","first-page":"957","volume":"8","author":"RA Hiatt","year":"1999","unstructured":"Hiatt, R.A., Rimer, B.K.: A new strategy for cancer control research. Cancer Epidemiol. Prev. Biomark. 8(11), 957\u2013964 (1999)","journal-title":"Cancer Epidemiol. Prev. Biomark."},{"issue":"1","key":"736_CR182","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/1476-072X-2-8","volume":"2","author":"JC Zubieta","year":"2003","unstructured":"Zubieta, J.C., Skinner, R., Dean, A.G.: Initiating informatics and gis support for a field investigation of bioterrorism: The new jersey anthrax experience. Int. J. Health. Geogr. 2(1), 8 (2003)","journal-title":"Int. J. Health. Geogr."},{"issue":"1","key":"736_CR183","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10916-006-7397-9","volume":"30","author":"TT Wan","year":"2006","unstructured":"Wan, T.T.: Healthcare informatics research: from data to evidence-based management. J. Med. Syst. 30(1), 3\u20137 (2006)","journal-title":"J. Med. Syst."},{"issue":"4","key":"736_CR184","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.jbi.2006.12.008","volume":"40","author":"D Revere","year":"2007","unstructured":"Revere, D., Turner, A.M., Madhavan, A., Rambo, N., Bugni, P.F., Kimball, A., Fuller, S.S.: Understanding the information needs of public health practitioners: a literature review to inform design of an interactive digital knowledge management system. J. Biomed. Inf. 40(4), 410\u2013421 (2007)","journal-title":"J. Biomed. Inf."},{"issue":"1","key":"736_CR185","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/2196-1115-1-2","volume":"1","author":"M Herland","year":"2014","unstructured":"Herland, M., Khoshgoftaar, T.M., Wald, R.: A review of data mining using big data in health informatics. J. Big Data 1(1), 2 (2014)","journal-title":"J. Big Data"},{"key":"736_CR186","unstructured":"Kamesh, D., Neelima, V., Priya, R.R.: A review of data mining using bigdata in health informatics. Int. J. Sci. Res, Publ. 5(3), (2015)"},{"issue":"1","key":"736_CR187","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/JBHI.2016.2636665","volume":"21","author":"D Rav\u0131","year":"2017","unstructured":"Rav\u0131, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.Z.: Deep learning for health informatics. IEEE J. Biomed. Health Inf. 21(1), 4\u201321 (2017)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"736_CR188","doi-asserted-by":"crossref","unstructured":"Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinform. (2017)","DOI":"10.1093\/bib\/bbx044"},{"issue":"1","key":"736_CR189","first-page":"78","volume":"12","author":"HA Aziz","year":"2017","unstructured":"Aziz, H.A.: A review of the role of public health informatics in healthcare. J. Taibah Univ. Med. Sci. 12(1), 78\u201381 (2017)","journal-title":"J. Taibah Univ. Med. Sci."},{"key":"736_CR190","unstructured":"Association, T.O.H.: https:\/\/www.ericsson.com\/491b06\/assets\/local\/mobility-report\/documents\/2019\/ericsson-mobility-report-q4-2019-update.pdf0 (2018). Accessed 29 Mar 2018"},{"key":"736_CR191","unstructured":"National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health. https:\/\/www.cdc.gov\/brfss\/about\/index.htm (2014). Accessed 29 Mar 2018"},{"issue":"1","key":"736_CR192","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1634\/theoncologist.12-1-20","volume":"12","author":"MJ Hayat","year":"2007","unstructured":"Hayat, M.J., Howlader, N., Reichman, M.E., Edwards, B.K.: Cancer statistics, trends, and multiple primary cancer analyses from the surveillance, epidemiology, and end results (seer) program. The oncologist 12(1), 20\u201337 (2007)","journal-title":"The oncologist"},{"key":"736_CR193","unstructured":"(NIH), N.C.I.: https:\/\/www.ericsson.com\/491b06\/assets\/local\/mobility-report\/documents\/2019\/ericsson-mobility-report-q4-2019-update.pdf2 (2018). Accessed 29 Mar 2018"},{"key":"736_CR194","unstructured":"Smith, C.A., Wicks, P.J.: Patientslikeme: Consumer health vocabulary as a folksonomy. In: AMIA annual symposium proceedings, vol. 2008, p. 682. American Medical Informatics Association (2008)"},{"key":"736_CR195","unstructured":"Heywood, J.: https:\/\/www.ericsson.com\/491b06\/assets\/local\/mobility-report\/documents\/2019\/ericsson-mobility-report-q4-2019-update.pdf3 (2005-2018). 18 Apr 2018"},{"key":"736_CR196","volume-title":"Human Mortality Database","author":"JR Wilmoth","year":"2010","unstructured":"Wilmoth, J.R., Shkolnikov, V.: Human Mortality Database. University of California, Berkeley (2010)"},{"key":"736_CR197","unstructured":"Shkolnikov, V., Barbieri, M., Wilmoth, J.: https:\/\/www.ericsson.com\/491b06\/assets\/local\/mobility-report\/documents\/2019\/ericsson-mobility-report-q4-2019-update.pdf4. Accessed 18 Apr 2018"},{"key":"736_CR198","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ypmed.2014.01.024","volume":"63","author":"SD Young","year":"2014","unstructured":"Young, S.D., Rivers, C., Lewis, B.: Methods of using real-time social media technologies for detection and remote monitoring of hiv outcomes. Prev. Med. 63, 112\u2013115 (2014)","journal-title":"Prev. Med."},{"issue":"4","key":"736_CR199","doi-asserted-by":"publisher","first-page":"e1001413","DOI":"10.1371\/journal.pmed.1001413","volume":"10","author":"SI Hay","year":"2013","unstructured":"Hay, S.I., George, D.B., Moyes, C.L., Brownstein, J.S.: Big data opportunities for global infectious disease surveillance. PLoS Med. 10(4), e1001413 (2013)","journal-title":"PLoS Med."},{"key":"736_CR200","doi-asserted-by":"crossref","unstructured":"Nambisan, P., Luo, Z., Kapoor, A., Patrick, T.B., Cisler, R.A.: Social media, big data, and public health informatics: Ruminating behavior of depression revealed through twitter. In: 2015 48th Hawaii International Conference on System Sciences, pp. 2906\u20132913. IEEE (2015)","DOI":"10.1109\/HICSS.2015.351"},{"key":"736_CR201","doi-asserted-by":"crossref","unstructured":"Tsugawa, S., Mogi, Y., Kikuchi, Y., Kishino, F., Fujita, K., Itoh, Y., Ohsaki, H.: On estimating depressive tendencies of twitter users utilizing their tweet data. In: Virtual Reality (VR), 2013 IEEE, pp. 1\u20134. IEEE (2013)","DOI":"10.1109\/VR.2013.6549431"},{"key":"736_CR202","unstructured":"Park, M., Cha, C., Cha, M.: Depressive moods of users portrayed in twitter. In: Proceedings of the ACM SIGKDD Workshop on Healthcare Informatics (HI-KDD), vol. 2012, pp. 1\u20138. ACM New York, NY (2012)"},{"key":"736_CR203","doi-asserted-by":"crossref","unstructured":"Park, S., Lee, S.W., Kwak, J., Cha, M., Jeong, B.: Activities on facebook reveal the depressive state of users. J. Med. Internet Res. 15(10), (2013)","DOI":"10.2196\/jmir.2718"},{"key":"736_CR204","first-page":"1","volume":"13","author":"M De Choudhury","year":"2013","unstructured":"De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. ICWSM 13, 1\u201310 (2013)","journal-title":"ICWSM"},{"key":"736_CR205","doi-asserted-by":"crossref","unstructured":"De\u00a0Choudhury, M., Counts, S., Horvitz, E.: Social media as a measurement tool of depression in populations. In: Proceedings of the 5th Annual ACM Web Science Conference, pp. 47\u201356. ACM (2013)","DOI":"10.1145\/2464464.2464480"},{"issue":"1","key":"736_CR206","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41398-020-0704-2","volume":"10","author":"SP Zoubovsky","year":"2020","unstructured":"Zoubovsky, S. P., Hoseus, S., Tumukuntala, S., Schulkin, J. O., Williams, M. T., Vorhees, C. V., et al. (2020). Chronicpsychosocial stress during pregnancy affects maternal behavior and neuroendocrine function and modulateshypothalamic CRH and nuclear steroid receptor expression. Translational psychiatry, 10(1), 1\u201313.","journal-title":"Translational psychiatry"},{"key":"736_CR207","doi-asserted-by":"crossref","unstructured":"De\u00a0Choudhury, M., Counts, S., Horvitz, E.J., Hoff, A.: Characterizing and predicting postpartum depression from shared facebook data. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 626\u2013638. ACM (2014)","DOI":"10.1145\/2531602.2531675"},{"key":"736_CR208","doi-asserted-by":"crossref","unstructured":"Sadilek, A., Kautz, H., Silenzio, V.: Modeling spread of disease from social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 6, no. 1, pp. 1\u20138 (2012)","DOI":"10.1609\/icwsm.v6i1.14235"},{"issue":"7232","key":"736_CR209","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1038\/nature07634","volume":"457","author":"J Ginsberg","year":"2009","unstructured":"Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012 (2009)","journal-title":"Nature"},{"key":"736_CR210","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.ijmedinf.2018.04.010","volume":"115","author":"E Hagg","year":"2018","unstructured":"Hagg, E., Dahinten, V.S., Currie, L.M.: The emerging use of social media for health-related purposes in low and middle-income countries: a scoping review. Int. J. Med. Inf. 115, 92\u2013105 (2018)","journal-title":"Int. J. Med. Inf."},{"key":"736_CR211","doi-asserted-by":"crossref","unstructured":"Belle, A., Thiagarajan, R., Soroushmehr, S., Navidi, F., Beard, D.A., Najarian, K.: Big data analytics in healthcare. BioMed Res. Int. (2015)","DOI":"10.1155\/2015\/370194"},{"issue":"6","key":"736_CR212","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s10877-013-9492-z","volume":"27","author":"M Bodo","year":"2013","unstructured":"Bodo, M., Settle, T., Royal, J., Lombardini, E., Sawyer, E., Rothwell, S.W.: Multimodal noninvasive monitoring of soft tissue wound healing. J. Clin. Monit. Comput. 27(6), 677\u2013688 (2013)","journal-title":"J. Clin. Monit. Comput."},{"issue":"1","key":"736_CR213","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.amj.2013.09.003","volume":"33","author":"P Hu","year":"2014","unstructured":"Hu, P., Galvagno, S.M., Sen, A., Dutton, R., Jordan, S., Floccare, D., Handley, C., Shackelford, S., Pasley, J., Mackenzie, C.: Identification of dynamic prehospital changes with continuous vital signs acquisition. Air Med. J. 33(1), 27\u201333 (2014)","journal-title":"Air Med. J."},{"issue":"10","key":"736_CR214","doi-asserted-by":"publisher","first-page":"e110274","DOI":"10.1371\/journal.pone.0110274","volume":"9","author":"BJ Drew","year":"2014","unstructured":"Drew, B.J., Harris, P., Z\u00e8gre-Hemsey, J.K., Mammone, T., Schindler, D., Salas-Boni, R., Bai, Y., Tinoco, A., Ding, Q., Hu, X.: Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of consecutive intensive care unit patients. PLoS One 9(10), e110274 (2014)","journal-title":"PLoS One"},{"issue":"1","key":"736_CR215","doi-asserted-by":"publisher","first-page":"28","DOI":"10.4037\/ajcc2010651","volume":"19","author":"KC Graham","year":"2010","unstructured":"Graham, K.C., Cvach, M.: Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am. J. Crit. Care 19(1), 28\u201334 (2010)","journal-title":"Am. J. Crit. Care"},{"issue":"4","key":"736_CR216","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1377\/hlthaff.2010.0155","volume":"29","author":"JS McCullough","year":"2010","unstructured":"McCullough, J.S., Casey, M., Moscovice, I., Prasad, S.: The effect of health information technology on quality in us hospitals. Health Affairs 29(4), 647\u2013654 (2010)","journal-title":"Health Affairs"},{"issue":"8","key":"736_CR217","doi-asserted-by":"publisher","first-page":"e6642","DOI":"10.1371\/journal.pone.0006642","volume":"4","author":"S Ahmad","year":"2009","unstructured":"Ahmad, S., Ramsay, T., Huebsch, L., Flanagan, S., McDiarmid, S., Batkin, I., McIntyre, L., Sundaresan, S.R., Maziak, D.E., Shamji, F.M., et al.: Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults. PLoS One 4(8), e6642 (2009)","journal-title":"PLoS One"},{"key":"736_CR218","unstructured":"Adri\u00e1n, G., Francisco, G.E., Marcela, M., Baum, A., Daniel, L., de\u00a0Quir\u00f3s\u00a0Fern\u00e1n, G.B.: Mongodb: an open source alternative for hl7-cda clinical documents management. In: Proceedings of the Open Source International Conference (CISL\u201913) (2013)"},{"issue":"3","key":"736_CR219","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/MC.2015.77","volume":"48","author":"K Kaur","year":"2015","unstructured":"Kaur, K., Rani, R.: Managing data in healthcare information systems: many models, one solution. Computer 48(3), 52\u201359 (2015)","journal-title":"Computer"},{"key":"736_CR220","unstructured":"Santos, M., Portela, F.: Enabling ubiquitous data mining in intensive care: features selection and data pre-processing. In: International Conference on Enterprise Information Systems, vol. 2, pp. 261\u2013266. SCITEPRESS (2011)"},{"issue":"12","key":"736_CR221","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/2.970578","volume":"34","author":"DJ Berndt","year":"2001","unstructured":"Berndt, D.J., Fisher, J.W., Hevner, A.R., Studnicki, J.: Healthcare data warehousing and quality assurance. Computer 34(12), 56\u201365 (2001)","journal-title":"Computer"},{"key":"736_CR222","doi-asserted-by":"publisher","first-page":"160035","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson, A.E., Pollard, T.J., Shen, L., Li-wei, H.L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L.A., Mark, R.G.: Mimic-iii, a freely accessible critical care database. Sci. Data 3, 160035 (2016)","journal-title":"Sci. Data"},{"issue":"23","key":"736_CR223","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000)","journal-title":"Circulation"},{"key":"736_CR224","doi-asserted-by":"crossref","unstructured":"Han, H., Ryoo, H.C., Patrick, H.: An infrastructure of stream data mining, fusion and management for monitored patients. In: Computer-Based Medical Systems, 2006. CBMS 2006. 19th IEEE International Symposium on, pp. 461\u2013468. IEEE (2006)","DOI":"10.1109\/CBMS.2006.39"},{"key":"736_CR225","doi-asserted-by":"crossref","unstructured":"Bressan, N., James, A., McGregor, C.: Trends and opportunities for integrated real time neonatal clinical decision support. In: Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on, pp. 687\u2013690. IEEE (2012)","DOI":"10.1109\/BHI.2012.6211676"},{"key":"736_CR226","unstructured":"Lee, J., Mark, R.: A hypotensive episode predictor for intensive care based on heart rate and blood pressure time series. In: Computing in Cardiology, 2010, pp. 81\u201384. IEEE (2010)"},{"key":"736_CR227","doi-asserted-by":"crossref","unstructured":"Sun, J., Sow, D., Hu, J., Ebadollahi, S.: A system for mining temporal physiological data streams for advanced prognostic decision support. In: Data Mining (ICDM), 2010 IEEE 10th International Conference on, pp. 1061\u20131066. IEEE (2010)","DOI":"10.1109\/ICDM.2010.102"},{"key":"736_CR228","unstructured":"Cao, H., Eshelman, L., Chbat, N., Nielsen, L., Gross, B., Saeed, M.: Predicting icu hemodynamic instability using continuous multiparameter trends. In: Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 3803\u20133806. IEEE (2008)"},{"issue":"2","key":"736_CR229","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12028-014-0041-5","volume":"21","author":"P Le Roux","year":"2014","unstructured":"Le Roux, P., Menon, D.K., Citerio, G., Vespa, P., Bader, M.K., Brophy, G.M., Diringer, M.N., Stocchetti, N., Videtta, W., Armonda, R., et al.: Consensus summary statement of the international multidisciplinary consensus conference on multimodality monitoring in neurocritical care. Neurocrit. Care 21(2), 1\u201326 (2014)","journal-title":"Neurocrit. Care"},{"issue":"2","key":"736_CR230","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/THC-171173","volume":"26","author":"JP Rajan","year":"2018","unstructured":"Rajan, J.P., Rajan, S.E.: An internet of things based physiological signal monitoring and receiving system for virtual enhanced health care network. Technol. Health Care 26(2), 1\u20137 (2018)","journal-title":"Technol. Health Care"},{"key":"736_CR231","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.jbi.2018.01.009","volume":"79","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Zhang, Y., Yao, L., Song, H., Kos, A.: A sensor-based wrist pulse signal processing and lung cancer recognition. J. Biomed. Inf 79, 107\u2013116 (2018)","journal-title":"J. Biomed. Inf"},{"key":"736_CR232","doi-asserted-by":"crossref","unstructured":"Nanda, S.K., Lin, W.Y., Lee, M.Y., Chen, R.S.: A quantitative classification of essential and parkinson\u2019s tremor using wavelet transform and artificial neural network on semg and accelerometer signals. In: Networking, Sensing and Control (ICNSC), 2015 IEEE 12th International Conference on, pp. 399\u2013404. IEEE (2015)","DOI":"10.1109\/ICNSC.2015.7116070"},{"key":"736_CR233","volume-title":"Understanding and Managing the Complexity of Healthcare","author":"WB Rouse","year":"2014","unstructured":"Rouse, W.B., Serban, N.: Understanding and Managing the Complexity of Healthcare. MIT Press, Cambridge (2014)"},{"issue":"1","key":"736_CR234","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1186\/1756-0381-7-22","volume":"7","author":"EA Mohammed","year":"2014","unstructured":"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. 7(1), 22 (2014)","journal-title":"BioData Min."},{"issue":"2","key":"736_CR235","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1089\/big.2012.0002","volume":"1","author":"M Swan","year":"2013","unstructured":"Swan, M.: The quantified self: Fundamental disruption in big data science and biological discovery. Big Data 1(2), 85\u201399 (2013)","journal-title":"Big Data"},{"issue":"2","key":"736_CR236","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1080\/23808993.2016.1157686","volume":"1","author":"BE Huang","year":"2016","unstructured":"Huang, B.E., Mulyasasmita, W., Rajagopal, G.: The path from big data to precision medicine. Expert Rev. Precis. Med. Drug Dev. 1(2), 129\u2013143 (2016)","journal-title":"Expert Rev. Precis. Med. Drug Dev."},{"issue":"3","key":"736_CR237","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1089\/big.2013.0019","volume":"1","author":"PS Bradley","year":"2013","unstructured":"Bradley, P.S.: Implications of big data analytics on population health management. Big Data 1(3), 152\u2013159 (2013)","journal-title":"Big Data"},{"key":"736_CR238","unstructured":"Wang, W., Haerian, K., Salmasian, H., Harpaz, R., Chase, H., Friedman, C.: A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from pubmed citations. In: AMIA annual symposium proceedings, vol. 2011, p. 1464. American Medical Informatics Association (2011)"},{"key":"736_CR239","doi-asserted-by":"crossref","unstructured":"Hung, C.L., Lin, Y.L.: Implementation of a parallel protein structure alignment service on cloud. Int. J. Genom., (2013)","DOI":"10.1155\/2013\/439681"},{"key":"736_CR240","doi-asserted-by":"crossref","unstructured":"Wang, L., Chen, D., Ranjan, R., Khan, S.U., KolOdziej, J., Wang, J.: Parallel processing of massive eeg data with mapreduce. In: 2012 IEEE 18th International Conference on Parallel and Distributed Systems, pp. 164\u2013171. Ieee (2012)","DOI":"10.1109\/ICPADS.2012.32"},{"issue":"12","key":"736_CR241","doi-asserted-by":"publisher","first-page":"6603","DOI":"10.1118\/1.3660200","volume":"38","author":"B Meng","year":"2011","unstructured":"Meng, B., Pratx, G., Xing, L.: Ultrafast and scalable cone-beam ct reconstruction using mapreduce in a cloud computing environment. Med. Phys. 38(12), 6603\u20136609 (2011)","journal-title":"Med. Phys."},{"issue":"01","key":"736_CR242","doi-asserted-by":"publisher","first-page":"42","DOI":"10.15265\/IY-2014-0018","volume":"23","author":"N Peek","year":"2014","unstructured":"Peek, N., Holmes, J., Sun, J.: Technical challenges for big data in biomedicine and health: data sources, infrastructure, and analytics. Yearb. Med. Inf. 23(01), 42\u201347 (2014)","journal-title":"Yearb. Med. Inf."},{"key":"736_CR243","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.coisb.2017.07.007","volume":"4","author":"AT Maia","year":"2017","unstructured":"Maia, A.T., Sammut, S.J., Jacinta-Fernandes, A., Chin, S.F.: Big data in cancer genomics. Curr. Opin. Syst. Biol. 4, 78\u201384 (2017)","journal-title":"Curr. Opin. Syst. Biol."},{"issue":"3","key":"736_CR244","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.joad.2015.04.003","volume":"4","author":"HT Wong","year":"2015","unstructured":"Wong, H.T., Yin, Q., Guo, Y.Q., Murray, K., Zhou, D.H., Slade, D.: Big data as a new approach in emergency medicine research. J. Acute Dis. 4(3), 178\u2013179 (2015)","journal-title":"J. Acute Dis."},{"issue":"4","key":"736_CR245","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1109\/JBHI.2015.2406883","volume":"19","author":"M Viceconti","year":"2015","unstructured":"Viceconti, M., Hunter, P., Hose, R.: Big data, big knowledge: big data for personalized healthcare. IEEE J. Biomed. Health Inf. 19(4), 1209\u20131215 (2015)","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"9","key":"736_CR246","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1016\/j.jalz.2016.04.008","volume":"12","author":"H Geerts","year":"2016","unstructured":"Geerts, H., Dacks, P.A., Devanarayan, V., Haas, M., Khachaturian, Z.S., Gordon, M.F., Maudsley, S., Romero, K., Stephenson, D., Initiative, B.H.M., et al.: Big data to smart data in Alzheimer\u2019s disease: the brain health modeling initiative to foster actionable knowledge. Alzheimer\u2019s Dement. 12(9), 1014\u20131021 (2016)","journal-title":"Alzheimer\u2019s Dement."},{"key":"736_CR247","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ymeth.2016.08.010","volume":"111","author":"I El Naqa","year":"2016","unstructured":"El Naqa, I.: Perspectives on making big data analytics work for oncology. Methods 111, 32\u201344 (2016)","journal-title":"Methods"},{"key":"736_CR248","unstructured":"Lu, J., Xu, Q., Li, B., Yuan, X., Sato, K.: Image processing apparatus, image processing method and medical imaging device (2019). US Patent App. 10\/282,631"},{"issue":"1","key":"736_CR249","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s10278-017-9991-4","volume":"31","author":"C Karmonik","year":"2018","unstructured":"Karmonik, C., Boone, T.B., Khavari, R.: Workflow for visualization of neuroimaging data with an augmented reality device. J. Dig. Imaging 31(1), 26\u201331 (2018)","journal-title":"J. Dig. Imaging"},{"key":"736_CR250","doi-asserted-by":"publisher","first-page":"e283","DOI":"10.1016\/j.wneu.2018.02.174","volume":"114","author":"PA Glemser","year":"2018","unstructured":"Glemser, P.A., Engel, K., Simons, D., Steffens, J., Schlemmer, H.P., Orakcioglu, B.: A new approach for photorealistic visualization of rendered computed tomography images. World Neurosurg. 114, e283\u2013e292 (2018)","journal-title":"World Neurosurg."},{"key":"736_CR251","unstructured":"Yu, D., Engel, K.: Joint visualization of 3d reconstructed photograph and internal medical scan (2018). US Patent App. 10\/092,191"},{"issue":"sup1","key":"736_CR252","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/07853890.2018.1560057","volume":"51","author":"JA Jorge","year":"2019","unstructured":"Jorge, J.A., Sim\u00f5es Lopes, D.: Challenges and approaches to interactive visualization in healthcare workspaces. Ann. Med. 51(sup1), 22\u201322 (2019)","journal-title":"Ann. Med."},{"key":"736_CR253","doi-asserted-by":"publisher","first-page":"61050","DOI":"10.1109\/ACCESS.2018.2874382","volume":"6","author":"RW Liu","year":"2018","unstructured":"Liu, R.W., Ma, Q., Yu, S.C.H., Chui, K.T., Xiong, N.: Variational regularized tree-structured wavelet sparsity for cs-sense parallel imaging. IEEE Access 6, 61050\u201361064 (2018)","journal-title":"IEEE Access"},{"key":"736_CR254","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2019.03.022","volume":"125","author":"S Khan","year":"2019","unstructured":"Khan, S., Islam, N., Jan, Z., Din, I.U., Rodrigues, J.J.C.: A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit. Lett. 125, 1\u20136 (2019)","journal-title":"Pattern Recognit. Lett."},{"key":"736_CR255","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.future.2018.10.009","volume":"92","author":"S Lakshmanaprabu","year":"2019","unstructured":"Lakshmanaprabu, S., Mohanty, S.N., Shankar, K., Arunkumar, N., Ramirez, G.: Optimal deep learning model for classification of lung cancer on ct images. Future Gener. Comput. Syst. 92, 374\u2013382 (2019)","journal-title":"Future Gener. Comput. Syst."},{"key":"736_CR256","unstructured":"Razzak, I., Naz, S., Rehman, A., Khan, A., Zaib, A.: Improving coronavirus (covid-19) diagnosis using deep transfer learning. medRxiv (2020)"},{"key":"736_CR257","doi-asserted-by":"crossref","unstructured":"Grace, R.K., Manimegalai, R., Kumar, S.S.: Medical image retrieval system in grid using hadoop framework. In: 2014 International Conference on Computational Science and Computational Intelligence, vol.\u00a01, pp. 144\u2013148. IEEE (2014)","DOI":"10.1109\/CSCI.2014.31"},{"key":"736_CR258","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.future.2014.08.008","volume":"43","author":"CT Yang","year":"2015","unstructured":"Yang, C.T., Shih, W.C., Chen, L.T., Kuo, C.T., Jiang, F.C., Leu, F.Y.: Accessing medical image file with co-allocation hdfs in cloud. Future Gener. Comput. Syst. 43, 61\u201373 (2015)","journal-title":"Future Gener. Comput. Syst."},{"key":"736_CR259","doi-asserted-by":"crossref","unstructured":"Markonis, D., Schaer, R., Eggel, I., M\u00fcller, H., Depeursinge, A.: Using mapreduce for large-scale medical image analysis. In: 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology, pp. 1\u20131. IEEE (2012)","DOI":"10.1109\/HISB.2012.8"},{"issue":"1","key":"736_CR260","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.ejrad.2009.10.014","volume":"73","author":"M Benjamin","year":"2010","unstructured":"Benjamin, M., Aradi, Y., Shreiber, R.: From shared data to sharing workflow: merging pacs and teleradiology. Eur. J. Radiol. 73(1), 3\u20139 (2010)","journal-title":"Eur. J. Radiol."},{"issue":"9","key":"736_CR261","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1016\/j.ijmedinf.2012.05.011","volume":"81","author":"C Costa","year":"2012","unstructured":"Costa, C., Oliveira, J.L.: Telecardiology through ubiquitous internet services. Int. J. Med. Inf. 81(9), 612\u2013621 (2012)","journal-title":"Int. J. Med. Inf."},{"issue":"2","key":"736_CR262","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s13244-010-0059-y","volume":"2","author":"P Ross","year":"2011","unstructured":"Ross, P., Pohjonen, H.: Images crossing borders: image and workflow sharing on multiple levels. Insights Imaging 2(2), 141\u2013148 (2011)","journal-title":"Insights Imaging"},{"key":"736_CR263","unstructured":"Wang, F., Lee, R., Liu, Q., Aji, A., Zhang, X., Saltz, J.: Hadoopgis: A high performance query system for analytical medical imaging with mapreduce: Technical report. Emory University (2011)"},{"key":"736_CR264","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.neucom.2014.12.123","volume":"173","author":"Q Zou","year":"2016","unstructured":"Zou, Q., Zeng, J., Cao, L., Ji, R.: A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing 173, 346\u2013354 (2016)","journal-title":"Neurocomputing"},{"issue":"1","key":"736_CR265","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1186\/s40537-019-0241-0","volume":"6","author":"K Tadist","year":"2019","unstructured":"Tadist, K., Najah, S., Nikolov, N.S., Mrabti, F., Zahi, A.: Feature selection methods and genomic big data: a systematic review. J. Big Data 6(1), 79 (2019)","journal-title":"J. Big Data"},{"key":"736_CR266","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.jprot.2018.12.004","volume":"198","author":"M Lualdi","year":"2019","unstructured":"Lualdi, M., Fasano, M.: Statistical analysis of proteomics data: a review on feature selection. J. Proteom. 198, 18\u201326 (2019)","journal-title":"J. Proteom."},{"key":"736_CR267","doi-asserted-by":"crossref","unstructured":"David, S.K., Saeb, A.T., Rafiullah, M., Rubeaan, K.: Classification techniques and data mining tools used in medical bioinformatics. In: Big Data Governance and Perspectives in Knowledge Management, pp. 105\u2013126. IGI Global (2019)","DOI":"10.4018\/978-1-5225-7077-6.ch005"},{"key":"736_CR268","doi-asserted-by":"crossref","unstructured":"Devi, A.S., Maragatham, G.: Big genome data classification with random forests using variantspark. In: International Conference on Computer Networks and Communication Technologies, pp. 599\u2013614. Springer (2019)","DOI":"10.1007\/978-981-10-8681-6_55"},{"key":"736_CR269","doi-asserted-by":"crossref","unstructured":"Patel, D.T.: Big data analytics in bioinformatics. In: Biotechnology: Concepts, Methodologies, Tools, and Applications, pp. 1967\u20131984. IGI Global (2019)","DOI":"10.4018\/978-1-5225-8903-7.ch080"},{"key":"736_CR270","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.cmpb.2016.04.016","volume":"132","author":"Z Goli-Malekabadi","year":"2016","unstructured":"Goli-Malekabadi, Z., Sargolzaei-Javan, M., Akbari, M.K.: An effective model for store and retrieve big health data in cloud computing. Comput. Methods Programs Biomed. 132, 75\u201382 (2016)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"6","key":"736_CR271","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijdps.2014.5601","volume":"5","author":"SN Sultana","year":"2014","unstructured":"Sultana, S.N., Ramu, G., Reddy, B.E.: Cloud-based development of smart and connected data in healthcare application. Int. J. Distrib. Parallel Syst. 5(6), 1 (2014)","journal-title":"Int. J. Distrib. Parallel Syst."},{"issue":"1","key":"736_CR272","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1109\/TBME.2012.2222404","volume":"60","author":"C He","year":"2012","unstructured":"He, C., Fan, X., Li, Y.: Toward ubiquitous healthcare services with a novel efficient cloud platform. IEEE Trans. Biomed. Eng. 60(1), 230\u2013234 (2012)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"736_CR273","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, L., Liu, H., Lei, C.: Large-scale clinical data management and analysis system based on cloud computing. In: Frontier and Future Development of Information Technology in Medicine and Education, pp. 1575\u20131583. Springer (2014)","DOI":"10.1007\/978-94-007-7618-0_177"},{"key":"736_CR274","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.ins.2018.01.001","volume":"435","author":"J Chen","year":"2018","unstructured":"Chen, J., Li, K., Rong, H., Bilal, K., Yang, N., Li, K.: A disease diagnosis and treatment recommendation system based on big data mining and cloud computing. Inf. Sci. 435, 124\u2013149 (2018)","journal-title":"Inf. Sci."},{"key":"736_CR275","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.techfore.2015.12.019","volume":"126","author":"Y Wang","year":"2018","unstructured":"Wang, Y., Kung, L., Byrd, T.A.: Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Change 126, 3\u201313 (2018)","journal-title":"Technol. Forecast. Soc. Change"},{"issue":"8","key":"736_CR276","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1016\/j.im.2016.07.004","volume":"53","author":"M Gupta","year":"2016","unstructured":"Gupta, M., George, J.F.: Toward the development of a big data analytics capability. Inf. Manag. 53(8), 1049\u20131064 (2016)","journal-title":"Inf. Manag."},{"issue":"3","key":"736_CR277","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1108\/JKM-08-2015-0301","volume":"21","author":"Y Wang","year":"2017","unstructured":"Wang, Y., Byrd, T.A.: Business analytics-enabled decision-making effectiveness through knowledge absorptive capacity in health care. J. Knowl. Manag. 21(3), 517\u2013539 (2017)","journal-title":"J. Knowl. Manag."},{"issue":"3","key":"736_CR278","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1177\/0266666916652671","volume":"33","author":"MK Kim","year":"2017","unstructured":"Kim, M.K., Park, J.H.: Identifying and prioritizing critical factors for promoting the implementation and usage of big data in healthcare. Inf. Dev. 33(3), 257\u2013269 (2017)","journal-title":"Inf. Dev."},{"issue":"12","key":"736_CR279","doi-asserted-by":"publisher","first-page":"2309","DOI":"10.3390\/su9122309","volume":"9","author":"KT Chui","year":"2017","unstructured":"Chui, K.T., Alhalabi, W., Pang, S.S.H., Pablos, P.O.D., Liu, R.W., Zhao, M.: Disease diagnosis in smart healthcare: innovation, technologies and applications. Sustainability 9(12), 2309 (2017)","journal-title":"Sustainability"},{"issue":"5","key":"736_CR280","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1002\/bult.2013.1720390508","volume":"39","author":"T Schultz","year":"2013","unstructured":"Schultz, T.: Turning healthcare challenges into big data opportunities: a use-case review across the pharmaceutical development lifecycle. Bull. Am. Soc. Inf. Sci. Technol. 39(5), 34\u201340 (2013)","journal-title":"Bull. Am. Soc. Inf. Sci. Technol."},{"key":"736_CR281","unstructured":"Sobhy, D., El-Sonbaty, Y., Elnasr, M.A.: Medcloud: healthcare cloud computing system. In: 2012 International Conference for Internet Technology and Secured Transactions, pp. 161\u2013166. IEEE (2012)"},{"key":"736_CR282","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.jss.2014.05.068","volume":"102","author":"W Lin","year":"2015","unstructured":"Lin, W., Dou, W., Zhou, Z., Liu, C.: A cloud-based framework for home-diagnosis service over big medical data. J. Syst. Softw. 102, 192\u2013206 (2015)","journal-title":"J. Syst. Softw."},{"key":"736_CR283","doi-asserted-by":"crossref","unstructured":"Seth, B., Dalal, S., Kumar, R.: Securing bioinformatics cloud for big data: Budding buzzword or a glance of the future. In: Recent Advances in Computational Intelligence, pp. 121\u2013147. Springer (2019)","DOI":"10.1007\/978-3-030-12500-4_8"},{"issue":"1","key":"736_CR284","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s13347-017-0278-y","volume":"32","author":"C Garattini","year":"2019","unstructured":"Garattini, C., Raffle, J., Aisyah, D.N., Sartain, F., Kozlakidis, Z.: Big data analytics, infectious diseases and associated ethical impacts. Philos. Technol. 32(1), 69\u201385 (2019)","journal-title":"Philos. Technol."},{"issue":"1","key":"736_CR285","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/1472-6947-14-44","volume":"14","author":"A Lamarche-Vadel","year":"2014","unstructured":"Lamarche-Vadel, A., Pavillon, G., Aouba, A., Johansson, L.A., Meyer, L., Jougla, E., Rey, G.: Automated comparison of last hospital main diagnosis and underlying cause of death icd10 codes, France, 2008\u20132009. BMC Med. Inf. Decis. Mak. 14(1), 44 (2014)","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"736_CR286","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/j.procs.2015.08.536","volume":"64","author":"J Cunha","year":"2015","unstructured":"Cunha, J., Silva, C., Antunes, M.: Health twitter big bata management with hadoop framework. Proc. Comput. Sci. 64, 425\u2013431 (2015)","journal-title":"Proc. Comput. Sci."},{"issue":"01","key":"736_CR287","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1055\/s-0038-1667081","volume":"27","author":"R Gamache","year":"2018","unstructured":"Gamache, R., Kharrazi, H., Weiner, J.P.: Public and population health informatics: the bridging of big data to benefit communities. Yearb. Med. Inf. 27(01), 199\u2013206 (2018)","journal-title":"Yearb. Med. Inf."},{"issue":"1","key":"736_CR288","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1186\/s12874-019-0796-7","volume":"19","author":"P Van Schaik","year":"2019","unstructured":"Van Schaik, P., Peng, Y., Ojelabi, A., Ling, J.: Explainable statistical learning in public health for policy development: the case of real-world suicide data. BMC Med. Res. Methodol. 19(1), 152 (2019)","journal-title":"BMC Med. Res. Methodol."},{"key":"736_CR289","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.ijmedinf.2019.01.012","volume":"124","author":"E Hatef","year":"2019","unstructured":"Hatef, E., Weiner, J.P., Kharrazi, H.: A public health perspective on using electronic health records to address social determinants of health: the potential for a national system of local community health records in the United States. Int. J. Med. Inf. 124, 86\u201389 (2019)","journal-title":"Int. J. Med. Inf."},{"issue":"4","key":"736_CR290","doi-asserted-by":"publisher","first-page":"e50","DOI":"10.2196\/mental.5842","volume":"3","author":"EM Seabrook","year":"2016","unstructured":"Seabrook, E.M., Kern, M.L., Rickard, N.S.: Social networking sites, depression, and anxiety: a systematic review. JMIR Ment. Health 3(4), e50 (2016)","journal-title":"JMIR Ment. Health"},{"key":"736_CR291","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.copsyc.2016.01.004","volume":"9","author":"M Conway","year":"2016","unstructured":"Conway, M., O\u2019Connor, D.: Social media, big data, and mental health: current advances and ethical implications. Curr. Opin. Psychol. 9, 77\u201382 (2016)","journal-title":"Curr. Opin. Psychol."},{"issue":"4","key":"736_CR292","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.genhosppsych.2013.03.008","volume":"35","author":"DC Mohr","year":"2013","unstructured":"Mohr, D.C., Burns, M.N., Schueller, S.M., Clarke, G., Klinkman, M.: Behavioral intervention technologies: evidence review and recommendations for future research in mental health. Gener. Hosp. Psychiatry 35(4), 332\u2013338 (2013)","journal-title":"Gener. Hosp. Psychiatry"},{"issue":"1","key":"736_CR293","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1097\/HCM.0000000000000194","volume":"37","author":"N Bhardwaj","year":"2018","unstructured":"Bhardwaj, N., Wodajo, B., Spano, A., Neal, S., Coustasse, A.: The impact of big data on chronic disease management. Health Care Manag 37(1), 90\u201398 (2018)","journal-title":"Health Care Manag"},{"issue":"2","key":"736_CR294","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1161\/CIRCOUTCOMES.114.001416","volume":"8","author":"JV Tu","year":"2015","unstructured":"Tu, J.V., Chu, A., Donovan, L.R., Ko, D.T., Booth, G.L., Tu, K., Maclagan, L.C., Guo, H., Austin, P.C., Hogg, W., et al.: The cardiovascular health in ambulatory care research team (canheart) using big data to measure and improve cardiovascular health and healthcare services. Circ. Cardiovasc. Qual. Outcomes 8(2), 204\u2013212 (2015)","journal-title":"Circ. Cardiovasc. Qual. Outcomes"},{"issue":"Suppl1","key":"736_CR295","doi-asserted-by":"publisher","first-page":"w156","DOI":"10.1377\/hlthaff.26.2.w156","volume":"26","author":"J Kupersmith","year":"2007","unstructured":"Kupersmith, J., Francis, J., Kerr, E., Krein, S., Pogach, L., Kolodner, R.M., Perlin, J.B.: Advancing evidence-based care for diabetes: Lessons from the veterans health administration: A highly regarded ehr system is but one contributor to the quality transformation of the vha since the mid-1990s. Health Affairs 26(Suppl1), w156\u2013w168 (2007)","journal-title":"Health Affairs"},{"key":"736_CR296","unstructured":"Consortium, I.H.G.S., et\u00a0al.: Initial sequencing and analysis of the human genome. Nature 409(6822), 860 (2001)"},{"key":"736_CR297","unstructured":"Energy, U.: Insights learned from the human dna sequence, what has been learned from analysis of the working draft sequence of the human genome? what is still unknown? Online. http:\/\/www. ornl. gov\/hgmis, Accessed 2 May 2011"},{"key":"736_CR298","doi-asserted-by":"crossref","unstructured":"Hey, A.J., Trefethen, A.E.: The data deluge: an e-science perspective. (2003)","DOI":"10.1002\/0470867167.ch36"},{"issue":"6","key":"736_CR299","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/MPUL.2011.942929","volume":"2","author":"F Ritter","year":"2011","unstructured":"Ritter, F., Boskamp, T., Homeyer, A., Laue, H., Schwier, M., Link, F., Peitgen, H.O.: Medical image analysis. IEEE Pulse 2(6), 60\u201370 (2011)","journal-title":"IEEE Pulse"},{"key":"736_CR300","doi-asserted-by":"crossref","unstructured":"D\u2019Agostino\u00a0Sr, R.B., Grundy, S., Sullivan, L.M., Wilson, P., Group, C.R.P., et\u00a0al.: Validation of the framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. Jama 286(2), 180\u2013187 (2001)","DOI":"10.1001\/jama.286.2.180"},{"key":"736_CR301","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.procs.2016.08.087","volume":"96","author":"M Waqialla","year":"2016","unstructured":"Waqialla, M., Razzak, M.I.: An ontology-based framework aiming to support cardiac rehabilitation program. Proc. Comput. Sci. 96, 23\u201332 (2016)","journal-title":"Proc. Comput. Sci."},{"issue":"393","key":"736_CR302","first-page":"1168","volume":"6","author":"C Alexander","year":"2017","unstructured":"Alexander, C., Wang, L.: Big data analytics in heart attack prediction. J. Nurs. Care 6(393), 1168\u20132167 (2017)","journal-title":"J. Nurs. Care"},{"key":"736_CR303","doi-asserted-by":"crossref","unstructured":"Palaniappan, S., Awang, R.: Intelligent heart disease prediction system using data mining techniques. In: Computer Systems and Applications, 2008. AICCSA 2008. IEEE\/ACS International Conference on, pp. 108\u2013115. IEEE (2008)","DOI":"10.1109\/AICCSA.2008.4493524"},{"key":"736_CR304","doi-asserted-by":"crossref","unstructured":"Shamli, N., Sathiyabhama, B.: Parkinson\u2019s brain disease prediction using big data analytics (2016)","DOI":"10.5815\/ijitcs.2016.06.10"},{"key":"736_CR305","doi-asserted-by":"crossref","unstructured":"Razzak, I., Kamran, I., Naz, S.: Deep analysis of handwritten notes for early diagnosis of neurological disorders. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207087"},{"key":"736_CR306","doi-asserted-by":"crossref","unstructured":"Kamran, I., Naz, S., Razzak, I., Imran, M.: Handwriting dynamics assessment using deep neural network for early identification of Parkinson\u2019s disease. Future Gener. Comput. Syst. (2020)","DOI":"10.1016\/j.future.2020.11.020"},{"issue":"7","key":"736_CR307","first-page":"626","volume":"4","author":"SS Sadhana","year":"2014","unstructured":"Sadhana, S.S., Shetty, S.: Analysis of diabetic data set using hive and r. Int. J. Emerg. Technol. Adv. Eng. 4(7), 626\u20139 (2014)","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"issue":"6","key":"736_CR308","doi-asserted-by":"publisher","first-page":"385","DOI":"10.5455\/aim.2015.23.385-392","volume":"23","author":"T Daghistani","year":"2015","unstructured":"Daghistani, T., Al Shammari, R., Razzak, M.I.: Discovering diabetes complications: an ontology based model. Acta Inf. Med. 23(6), 385 (2015)","journal-title":"Acta Inf. Med."},{"key":"736_CR309","doi-asserted-by":"crossref","unstructured":"Panda, M., Ali, S.M., Panda, S.K.: Big data in health care: A mobile based solution. In: Big Data Analytics and Computational Intelligence (ICBDAC), 2017 International Conference on, pp. 149\u2013152. IEEE (2017)","DOI":"10.1109\/ICBDACI.2017.8070826"},{"issue":"3","key":"736_CR310","doi-asserted-by":"publisher","first-page":"195","DOI":"10.18043\/ncm.75.3.195","volume":"75","author":"SC Helm-Murtagh","year":"2014","unstructured":"Helm-Murtagh, S.C.: Use of big data by blue cross and blue shield of North Carolina. North Carol. Med. J. 75(3), 195\u2013197 (2014)","journal-title":"North Carol. Med. J."},{"issue":"1","key":"736_CR311","first-page":"97","volume":"26","author":"X Wu","year":"2013","unstructured":"Wu, X., Zhu, X., Wu, G.Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97\u2013107 (2013)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-020-00736-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-020-00736-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-020-00736-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T18:57:24Z","timestamp":1724353044000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-020-00736-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,21]]},"references-count":311,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["736"],"URL":"https:\/\/doi.org\/10.1007\/s00530-020-00736-8","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,21]]},"assertion":[{"value":"13 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}