Skip to main content

Advertisement

Log in

Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities

  • Special Issue Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Clinical decisions are more promising and evidence-based, hence, big data analytics to assist clinical decision-making has been expressed for a variety of clinical fields. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention, and helps us to improve the quality of life. Researchers and clinicians are working to inhibit big data from having a positive impact on health in the future. Different tools and techniques are being used to analyze, process, accumulate, assimilate, and manage large amount of healthcare data either in structured or unstructured form. In this review, we address the need of big data analytics in healthcare: why and how can it help to improve life?. We present the emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare, i.e., medical image analysis and imaging informatics, bioinformatics, clinical informatics, public health informatics and medical signal analytics. We present different architectures, advantages and repositories of each discipline that draws an integrated depiction of how distinct healthcare activities are accomplished in the pipeline to facilitate individual patients from multiple perspectives. Finally, the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Notes

  1. http://www.zdnet.com/blog/virtualization/what-is-big-data/1708

  2. https://www.techopedia.com/definition/27745/big-data

  3. Available at: http://www.en.nuk.usz.ch/expert-knowledge/PublishingImages/pages/pet-center/PETMR.png

References

  1. Frost, S.: Drowning in big data? reducing information technology complexities and costs for healthcare organizations (2015)

  2. Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 3 (2014)

    Article  Google Scholar 

  3. Baro, E., Degoul, S., Beuscart, R., Chazard, E.: Toward a literature-driven definition of big data in healthcare. BioMed Res. Int. (2015)

  4. Burghard, C.: Big data and analytics key to accountable care success. In: IDC Health Insights pp. 1–9 (2012)

  5. Dembosky, A.: Data prescription for better healthcare. Financial Times 11(12), 2012 (2012)

    Google Scholar 

  6. Feldman, B., Martin, E.M., Skotnes, T.: Big data in healthcare hype and hope. Dr. Bonnie 360, 122–125 (2012)

  7. Fernandes, L.M., O’Connor, M., Weaver, V.: Big data, bigger outcomes. J. AHIMA 83(10), 38–43 (2012)

    Google Scholar 

  8. Vayena, E., Salathé, M., Madoff, L.C., Brownstein, J.S.: Ethical challenges of big data in public health. PLoS Comput. Biol. 11(2), e1003904 (2015)

    Article  Google Scholar 

  9. 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), 203–208 (2015)

    Article  Google Scholar 

  10. Ward, M.J., Marsolo, K.A., Froehle, C.M.: Applications of business analytics in healthcare. Bus. Horizons 57(5), 571–582 (2014)

    Article  Google Scholar 

  11. Sessler, D.I.: Big data-and its contributions to peri-operative medicine. Anaesthesia 69(2), 100–105 (2014)

    Article  Google Scholar 

  12. Razzak, M.I., Imran, M., Xu, G.: Big data analytics for preventive medicine. Neural Comput. Appl., 1–35 (2019)

  13. 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)

  14. Available:: Internet world stats. https://www.internetworldstats.com/stats.htm (2018)

  15. Manogaran, G., Lopez, D.: A survey of big data architectures and machine learning algorithms in healthcare. Int. J. Biomed. Eng. Technol. 25(2–4), 182–211 (2017)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. Ackerman, M.J.: The visible human project: a resource for education. Acad. Med. 74(6), 667–670 (1999)

    Article  Google Scholar 

  18. 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. 154–160. Springer (2016)

  19. Razzak, M.I., Naz, S., Zaib, A.: Deep learning for medical image processing: Overview, challenges and the future. In: Classification in BioApps, pp. 323–350. Springer (2018)

  20. Cox, M., Ellsworth, D.: Application-controlled demand paging for out-of-core visualization. In: Proceedings of the 8th Conference on Visualization’97, pp. 235–ff. IEEE Computer Society Press (1997)

  21. Kochański, A.: Data preparation. Comput. Methods Mater. Sci. 10(1), 25–29 (2010)

    Google Scholar 

  22. 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, ”(edited by M. Dewatripont, LP Hansen and S. Turnovsky), pp. 115–122 (2003)

  23. Laney, D.: 3d data management: Controlling data volume, velocity and variety. META Group Res. Note 6(70), 1 (2001)

    Google Scholar 

  24. O’Reilly, T., Steele, J., Loukides, M., Hill, C.: Solving the wanamaker problem for healthcare (2012)

  25. Shin, D.: Demystifying big data: anatomy of big data developmental process. Telecommun. Policy 40(9), 837–854 (2016)

    Article  Google Scholar 

  26. Emani, C.K., Cullot, N., Nicolle, C.: Understandable big data: a survey. Comput. Sci. Rev. 17, 70–81 (2015)

    Article  MathSciNet  Google Scholar 

  27. Chen, M., Mao, S., Liu, Y.: Big data: A survey. Mob. Netw. Appl. 19(2), 171–209 (2014)

    Article  Google Scholar 

  28. Groves, P., Kayyali, B., Knott, D., Van Kuiken, S.: The ‘big data’revolution in healthcare. McKinsey Q. 2, 3 (2013)

    Google Scholar 

  29. Eynon, R.: The rise of big data: what does it mean for education, technology, and media research? (2013)

  30. Porche, D.J.: Men’s health big data (2014)

  31. Berger, M.L., Doban, V.: Big data, advanced analytics and the future of comparative effectiveness research. J. Comp. Effect. Res. 3(2), 167–176 (2014)

    Article  Google Scholar 

  32. 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. 65. IOS Press (2014)

  33. Watson, H.J.: Tutorial: Big data analytics: concepts, technologies, and applications. CAIS 34, 65 (2014)

    Article  Google Scholar 

  34. McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D., Barton, D.: Big data: the management revolution. Harv. Bus. Rev. 90(10), 60–68 (2012)

    Google Scholar 

  35. Russom, P., et al.: Big data analytics. TDWI Best Pract. Rep. Fourth Quarter 19(4), 1–34 (2011)

    Google Scholar 

  36. Saporito: The 5 v’s 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)

  37. Sathi, A.: Big Data Analytics: Disruptive Technologies for Changing the Game. MC Press, Chennai (2012)

    Google Scholar 

  38. Manogaran, G., Lopez, D.: Health data analytics using scalable logistic regression with stochastic gradient descent. Int. J. Adv. Intel. Paradig. 10(1–2), 118–132 (2018)

    Google Scholar 

  39. Tsai, C.W., Lai, C.F., Chao, H.C., Vasilakos, A.V.: Big data analytics: a survey. J. Big Data 2(1), 1–32 (2015)

    Article  Google Scholar 

  40. James, R.: Out of the box: Big data needs the information profession-the importance of validation. Bus. Inf. Rev. 31(2), 118–121 (2014)

    Google Scholar 

  41. Gandomi, A., Haider, M.: Beyond the hype: Big data concepts, methods, and analytics. Int. J. Inf. Manag. 35(2), 137–144 (2015)

    Article  Google Scholar 

  42. 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. 1–7. IEEE (2018)

  43. Naz, S., Umar, A.I., Ahmad, R., Siddiqi, I., Ahmed, S.B., Razzak, M.I., Shafait, F.: Urdu nastaliq recognition using convolutional–recursive deep learning. Neurocomputing 243, 80–87 (2017)

    Article  Google Scholar 

  44. 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)

  45. 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, 228–241 (2016)

    Article  Google Scholar 

  46. Holland, S.M.: Principal components analysis (pca), pp. 30602–2501. Department of Geology, University of Georgia, Athens, GA pp (2008)

  47. SVD, S.V.D.: Singular value decomposition. 593–594 (2014)

  48. Schölkopf, B., Smola, A., Müller, K.R.: Kernel principal component analysis. In: International Conference on Artificial Neural Networks, pp. 583–588. Springer (1997)

  49. Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Trans. Comput. 100(5), 401–409 (1969)

    Article  Google Scholar 

  50. De Ridder, D., Duin, R.P.: Sammon’s mapping using neural networks: a comparison. Pattern Recognit. Lett. 18(11–13), 1307–1316 (1997)

    Article  Google Scholar 

  51. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)

    Article  MATH  Google Scholar 

  52. Pareto, V.: Cours d’économie politique, vol. 1. Librairie Droz, Geneva (1964)

    Book  Google Scholar 

  53. 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. 82–87. Ieee (1994)

  54. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms, vol. 16. Wiley, Hoboken (2001)

    MATH  Google Scholar 

  55. Bäck, T.: Evolutionary computation: toward a new philosophy of machine intelligence (1997)

  56. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. Wiley, Hoboken (2009)

    MATH  Google Scholar 

  57. Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. ACM Sigmod Rec. 25(2), 103–114 (1996)

    Article  Google Scholar 

  58. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  59. 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. 1222–1230. ACM (2012)

  60. 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. 1–10. IEEE (2010)

  61. Capriolo, E., Wampler, D., Rutherglen, J.: Programming Hive: Data Warehouse and Query Language for Hadoop. O’Reilly Media Inc, Newton (2012)

    Google Scholar 

  62. Wulff, F.: Presto. https://prestodb.io/ (2013)

  63. Hortonworks: Apache mahout. http://hortonworks.com/hadoop/mahout/ (2015)

  64. Confluent: Avro. http://docs.confluent.io/1.0/avro.html (2015)

  65. 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)

  66. Wamba, S.F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D.: How ‘big data’can make big impact: findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 165, 234–246 (2015)

    Article  Google Scholar 

  67. 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), 75–83 (2017)

    Article  Google Scholar 

  68. Jacofsky, D.J.: The myths of ‘big data’ in health care. Bone Jt J. 99(12), 1571–1576 (2017)

    Article  Google Scholar 

  69. 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), 64–79 (2018)

    Google Scholar 

  70. Galetsi, P., Katsaliaki, K.: A review of the literature on big data analytics in healthcare. J. Oper. Res. Soc., 1–19 (2019)

  71. 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), 757–775 (2020)

    Article  Google Scholar 

  72. 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

  73. Rehman, A., Khan, F.G.: A deep learning based review on abdominal images. Multimed. Tools Appl. 1–32 (2020)

  74. 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), 335–347 (2016)

    Article  Google Scholar 

  75. Razzak, I., Imran, M., Xu, G.: Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE J. Biomed. Health Inf. (2018)

  76. Naz, S., Umar, A.I., Ahmad, R., Ahmed, S.B., Shirazi, S.H., Razzak, M.I.: Urdu nasta’liq text recognition system based on multi-dimensional recurrent neural network and statistical features. Neural Comput. Appl. 28(2), 219–231 (2017)

    Article  Google Scholar 

  77. 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)

    Google Scholar 

  78. Shackelford, K.: System & method for delineation and quantification of fluid accumulation in efast trauma ultrasound images. US Patent App. 14/167,448 (2014)

  79. 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. 510–515. IEEE (2010)

  80. 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), 216–222 (2014)

    Google Scholar 

  81. 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. 1–5. IEEE (2013)

  82. 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)

    Article  Google Scholar 

  83. Istephan, S., Siadat, M.R.: Unstructured medical image query using big data—an epilepsy case study. J. Biomed. Inf. 59, 218–226 (2016)

    Article  Google Scholar 

  84. O’Driscoll, A., Daugelaite, J., Sleator, R.D.: ‘big data’, hadoop and cloud computing in genomics. J. Biomed. Inf. 46(5), 774–781 (2013)

    Article  MATH  Google Scholar 

  85. Robison, R.J.: https://medium.com/precision-medicine/how-big-is-the-human-genome-e90caa3409b0 (2014)

  86. 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), 837–854 (2014)

    Google Scholar 

  87. Lander Eric, S., Linton Lauren, M., Bruce, B., Chad, N., Zody Michael, C., Jennifer, B., Keri, D., Ken, D., Michael, D., William, F., et al.: Initial sequencing and analysis of the human genome. (2001)

  88. 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), 78–81 (2010)

    Article  Google Scholar 

  89. Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012)

    Article  Google Scholar 

  90. Priyanka, K., Kulennavar, N.: A survey on big data analytics in health care. Int. J. Comput. Sci. Inf. Technol. 5(4), 5865–5868 (2014)

    Google Scholar 

  91. 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), 27–38 (2013)

    Article  Google Scholar 

  92. Kanz, C., Aldebert, P., Althorpe, N., Baker, W., Baldwin, A., Bates, K., Browne, P., van den Broek, A., Castro, M., Cochrane, G., et al.: The embl nucleotide sequence database. Nucl. Acids Res. 33(suppl\_1), D29–D33 (2005)

  93. Bilofsky, H.S., Christian, B.: The genbank® genetic sequence data bank. Nucl. Acids Res. 16(5), 1861–1863 (1988)

    Article  Google Scholar 

  94. Yao, Y.G., Salas, A., Logan, I., Bandelt, H.J.: mtdna data mining in genbank needs surveying. Am. J. Hum. Genet. 85(6), 929–933 (2009)

    Article  Google Scholar 

  95. Sugawara, H., Ogasawara, O., Okubo, K., Gojobori, T., Tateno, Y.: DDBJ with new system and face. Nucl. Acids Res. 36(suppl_1), D22–D24 (2007)

  96. Letovsky, S.I., Cottingham, R.W., Porter, C.J., Li, P.W.: Gdb: the human genome database. Nucl. Acids Res. 26(1), 94–99 (1998)

    Article  Google Scholar 

  97. 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(10–11), 882–899 (2005)

    Article  Google Scholar 

  98. Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.C., Estreicher, A., Gasteiger, E., Martin, M.J., Michoud, K., O’donovan, C., Phan, I., et al.: The swiss-prot protein knowledgebase and its supplement trembl in 2003. Nucl. Acids Res. 31(1), 365–370 (2003)

    Article  Google Scholar 

  99. GDB: http://www.bioinfo.pte.hu/more/TrEMBL.htm. Accessed 15 Mar 2018

  100. Hulo, N., Bairoch, A., Bulliard, V., Cerutti, L., De Castro, E., Langendijk-Genevaux, P.S., Pagni, M., Sigrist, C.J.: The prosite database. Nucl. Acids Res. 34(suppl\_1), D227–D230 (2006)

  101. Kouranov, A., Xie, L., de la 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–D305 (2006)

  102. 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)

    Article  Google Scholar 

  103. 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. 173–204. Springer (2015)

  104. Barbu, A., She, Y., Ding, L., Gramajo, G.: Feature selection with annealing for big data learning. arXiv preprint (2013)

  105. 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, 39–60 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  106. Mitchell, T.M., et al.: Machine learning. 1997. Burr Ridge, IL: McGraw Hill 45(37), 870–877 (1997)

  107. Duda, R.O., Hart, P.E., Stork, D.G., et al.: Pattern Classification, vol. 2. Wiley, New York (1973)

    MATH  Google Scholar 

  108. Anzai, Y.: Pattern Recognition and Machine Learning. Elsevier, Amsterdam (2012)

    MATH  Google Scholar 

  109. Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. MIT Press, Cambridge (2012)

    MATH  Google Scholar 

  110. Hsieh, C.J., Si, S., Dhillon, I.: A divide-and-conquer solver for kernel support vector machines. In: International Conference on Machine Learning, pp. 566–574 (2014)

  111. Djuric, N.: Big Data Algorithms for Visualization and Supervised Learning. Temple University, Philadelphia (2013)

    Google Scholar 

  112. 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)

  113. 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), 2123–2128 (2012)

    Article  Google Scholar 

  114. 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), 253–259 (2010)

    Article  Google Scholar 

  115. 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. 34–38 (2008)

  116. 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. 2061–2064. ACM (2009)

  117. Calaway, R., Edlefsen, L., Gong, L., Fast, S.: Big data decision trees with r. Revolution (2016)

  118. 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. 3, pp. 2579–2584. IEEE (1998)

  119. Ng, R.T., Han, J.: Clarans: a method for clustering objects for spatial data mining. IEEE Trans. Knowl. Data Eng. 14(5), 1003–1016 (2002)

    Article  Google Scholar 

  120. 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), 226–231 (1996)

    Google Scholar 

  121. Hinneburg, A., Keim, D.A., et al.: An efficient approach to clustering in large multimedia databases with noise. KDD 98, 58–65 (1998)

    Google Scholar 

  122. Guha, S., Rastogi, R., Shim, K.: CURE: an efficient clustering algorithm for large databases. ACM Sigmod Rec. 27(2), 73–84 (1998)

    Article  MATH  Google Scholar 

  123. Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discov. 2(3), 283–304 (1998)

    Article  Google Scholar 

  124. Xu, X., Jäger, J., Kriegel, H.P.: A fast parallel clustering algorithm for large spatial databases. In: High Performance Data Mining, pp. 263–290. Springer (1999)

  125. Chen, N., Chen, A.Z., Zhou, L.X.: An incremental grid density-based clustering algorithm. J. Softw. 13(1), 1–7 (2002)

    Google Scholar 

  126. Kumar, V., Sharma, R.M., Thakur, R.: Big data analytics: Bioinformatics perspective. (2016)

  127. 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), 1068–1080 (2007)

    Article  Google Scholar 

  128. 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), 3364–3367 (2013)

    Article  Google Scholar 

  129. 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)

    Article  Google Scholar 

  130. 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)

    Article  Google Scholar 

  131. Langfelder, P., Horvath, S.: Wgcna: an r package for weighted correlation network analysis. BMC Bioinform. 9(1), 559 (2008)

    Article  Google Scholar 

  132. Rivera, C.G., Vakil, R., Bader, J.S.: Nemo: network module identification in cytoscape. BMC Bioinform. 11(1), S61 (2010)

    Article  Google Scholar 

  133. Bader, G.D., Hogue, C.W.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 4(1), 2 (2003)

    Article  Google Scholar 

  134. Nepusz, T., Yu, H., Paccanaro, A.: Detecting overlapping protein complexes in protein-protein interaction networks. Nat. Methods 9(5), 471 (2012)

    Article  Google Scholar 

  135. 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–W88 (2004)

  136. 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), 2209–2210 (2012)

    Article  Google Scholar 

  137. 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), 1–9 (2008)

    Google Scholar 

  138. 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), 808–814 (2013)

    Article  Google Scholar 

  139. 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), 1121–1126 (2002)

    Article  Google Scholar 

  140. 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)

    Article  Google Scholar 

  141. Luo, W., Brouwer, C.: Pathview: an r/bioconductor package for pathway-based data integration and visualization. Bioinformatics 29(14), 1830–1831 (2013)

    Article  Google Scholar 

  142. Schatz, M.C.: Cloudburst: highly sensitive read mapping with mapreduce. Bioinformatics 25(11), 1363–1369 (2009)

    Article  Google Scholar 

  143. Schatz, M., Sommer, D., Kelley, D., Pop, M.: Contrail: Assembly of large genomes using cloud computing. In: CSHL Biology of Genomes Conference (2010)

  144. Gurtowski, J., Schatz, M.C., Langmead, B.: Genotyping in the cloud with crossbow. Curr. Protoc. Bioinform., 3–15 (2012)

  145. 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)

    Article  Google Scholar 

  146. O’Connor, D.B., Merriman, B., Nelson, S.F.: Seqware query engine: storing and searching sequence data in the cloud. BMC Inform 11(12), S2 (2010)

    Google Scholar 

  147. George, L.: HBase: The Definitive Guide: Random Access to Your Planet-Size Data. O’Reilly Media, Inc., Newton (2011)

    Google Scholar 

  148. Robinson, T., Killcoyne, S., Bressler, R., Boyle, J.: SAMQA: error classification and validation of high-throughput sequenced read data. BMC Genom. 12(1), 1–7 (2011)

    Article  Google Scholar 

  149. Huang, W., Li, L., Myers, J.R., Marth, G.T.: Art: a next-generation sequencing read simulator. Bioinformatics 28(4), 593–594 (2011)

    Article  Google Scholar 

  150. 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. 717–722. IEEE (2013)

  151. 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), 1513–1518 (2011)

    Article  Google Scholar 

  152. 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)

    Article  Google Scholar 

  153. Eelmets, M.: Clovr: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing. – -(-) (2011)

  154. 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)

    Article  Google Scholar 

  155. 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), 1297–1303 (2010)

    Article  Google Scholar 

  156. 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, 10–11 (2013)

    Google Scholar 

  157. Huang, H., Tata, S., Prill, R.J.: Bluesnp: R package for highly scalable genome-wide association studies using hadoop clusters. Bioinformatics 29(1), 135–136 (2012)

    Article  Google Scholar 

  158. Abbott, P.A., Coenen, A.: Globalization and advances in information and communication technologies: the impact on nursing and health. Nurs Outlook 56(5), 238–246 (2008)

    Article  Google Scholar 

  159. Bhattacherjee, A., Hikmet, N.: Physicians’ resistance toward healthcare information technology: a theoretical model and empirical test. Eur. J. Inf. Syst. 16(6), 725–737 (2007)

    Article  Google Scholar 

  160. Blumenthal, D.: Launching hitech. N. Engl. J. Med. 362(5), 382–385 (2010)

    Article  Google Scholar 

  161. Bakshi, K.: Considerations for big data: architecture and approach. In: Aerospace Conference, 2012 IEEE, pp. 1–7. IEEE (2012)

  162. 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, 261–272 (2011)

    Google Scholar 

  163. 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, 464–471 (2011)

    Article  Google Scholar 

  164. 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. 331–347. Springer (2011)

  165. 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. 2, pp. 369–372. IEEE (2011)

  166. 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)

  167. Jayapandian, C.P., Chen, C.H., Bozorgi, A., Lhatoo, S.D., Zhang, G.Q., Sahoo, S.S.: Cloudwave: distributed processing of “big data” from electrophysiological recordings for epilepsy clinical research using hadoop. In: AMIA Annual Symposium Proceedings, vol. 2013, p. 691. American Medical Informatics Association (2013)

  168. 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 ‘big data’using cloud computing for epilepsy clinical research. J. Am. Med. Inf. Assoc. 21(2), 263–271 (2013)

    Article  Google Scholar 

  169. Mazurek, M.: Applying nosql databases for operationalizing clinical data mining models. In: International Conference: Beyond Databases, Architectures and Structures, pp. 527–536. Springer (2014)

  170. Bahga, A., Madisetti, V.K.: A cloud-based approach for interoperable electronic health records (ehrs). IEEE J. Biomed. Health Inf. 17(5), 894–906 (2013)

    Article  Google Scholar 

  171. Chen, J., Qian, F., Yan, W., Shen, B.: Translational biomedical informatics in the cloud: present and future. BioMed Res. Int., (2013)

  172. 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. 64–68. ACM (2011)

  173. 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, 160–170 (2014)

    Article  Google Scholar 

  174. Chawla, N.V., Davis, D.A.: Bringing big data to personalized healthcare: a patient-centered framework. J. Gener. Intern. Med. 28(3), 660–665 (2013)

    Article  Google Scholar 

  175. Abbott, R.: Big data and pharmacovigilance: using health information exchanges to revolutionize drug safety. Iowa L. Rev. 99, 225 (2013)

    Google Scholar 

  176. 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. 64–71. IEEE (2013)

  177. 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. 65–79. Springer (2015)

  178. Wang, Y., Hajli, N.: Exploring the path to big data analytics success in healthcare. J. Bus. Res. 70, 287–299 (2017)

    Article  Google Scholar 

  179. 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)

    Article  Google Scholar 

  180. 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), 1117–1121 (1999)

    Google Scholar 

  181. Hiatt, R.A., Rimer, B.K.: A new strategy for cancer control research. Cancer Epidemiol. Prev. Biomark. 8(11), 957–964 (1999)

    Google Scholar 

  182. 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)

    Article  Google Scholar 

  183. Wan, T.T.: Healthcare informatics research: from data to evidence-based management. J. Med. Syst. 30(1), 3–7 (2006)

    Article  Google Scholar 

  184. 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), 410–421 (2007)

    Article  Google Scholar 

  185. 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)

    Article  Google Scholar 

  186. 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)

  187. Ravı, 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), 4–21 (2017)

    Article  Google Scholar 

  188. Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinform. (2017)

  189. Aziz, H.A.: A review of the role of public health informatics in healthcare. J. Taibah Univ. Med. Sci. 12(1), 78–81 (2017)

    Google Scholar 

  190. 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

  191. 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

  192. 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), 20–37 (2007)

    Article  Google Scholar 

  193. (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

  194. 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)

  195. 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

  196. Wilmoth, J.R., Shkolnikov, V.: Human Mortality Database. University of California, Berkeley (2010)

    Google Scholar 

  197. 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

  198. 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, 112–115 (2014)

    Article  Google Scholar 

  199. 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)

    Article  Google Scholar 

  200. 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. 2906–2913. IEEE (2015)

  201. 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. 1–4. IEEE (2013)

  202. 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. 1–8. ACM New York, NY (2012)

  203. 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)

  204. De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. ICWSM 13, 1–10 (2013)

    Google Scholar 

  205. De Choudhury, 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. 47–56. ACM (2013)

  206. 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), 1–13.

    Article  Google Scholar 

  207. De Choudhury, 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. 626–638. ACM (2014)

  208. 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. 1–8 (2012)

  209. 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)

    Article  Google Scholar 

  210. 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, 92–105 (2018)

    Article  Google Scholar 

  211. Belle, A., Thiagarajan, R., Soroushmehr, S., Navidi, F., Beard, D.A., Najarian, K.: Big data analytics in healthcare. BioMed Res. Int. (2015)

  212. 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), 677–688 (2013)

    Article  Google Scholar 

  213. 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), 27–33 (2014)

    Article  Google Scholar 

  214. Drew, B.J., Harris, P., Zègre-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)

    Article  Google Scholar 

  215. Graham, K.C., Cvach, M.: Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am. J. Crit. Care 19(1), 28–34 (2010)

    Article  Google Scholar 

  216. McCullough, J.S., Casey, M., Moscovice, I., Prasad, S.: The effect of health information technology on quality in us hospitals. Health Affairs 29(4), 647–654 (2010)

    Article  Google Scholar 

  217. 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)

    Article  Google Scholar 

  218. Adrián, G., Francisco, G.E., Marcela, M., Baum, A., Daniel, L., de Quirós Fernán, G.B.: Mongodb: an open source alternative for hl7-cda clinical documents management. In: Proceedings of the Open Source International Conference (CISL’13) (2013)

  219. Kaur, K., Rani, R.: Managing data in healthcare information systems: many models, one solution. Computer 48(3), 52–59 (2015)

    Article  Google Scholar 

  220. 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. 261–266. SCITEPRESS (2011)

  221. Berndt, D.J., Fisher, J.W., Hevner, A.R., Studnicki, J.: Healthcare data warehousing and quality assurance. Computer 34(12), 56–65 (2001)

    Article  Google Scholar 

  222. 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)

    Article  Google Scholar 

  223. 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–e220 (2000)

    Article  Google Scholar 

  224. 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. 461–468. IEEE (2006)

  225. 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. 687–690. IEEE (2012)

  226. 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. 81–84. IEEE (2010)

  227. 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. 1061–1066. IEEE (2010)

  228. 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. 3803–3806. IEEE (2008)

  229. 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), 1–26 (2014)

    Article  Google Scholar 

  230. 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), 1–7 (2018)

    Article  Google Scholar 

  231. 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, 107–116 (2018)

    Article  Google Scholar 

  232. Nanda, S.K., Lin, W.Y., Lee, M.Y., Chen, R.S.: A quantitative classification of essential and parkinson’s 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. 399–404. IEEE (2015)

  233. Rouse, W.B., Serban, N.: Understanding and Managing the Complexity of Healthcare. MIT Press, Cambridge (2014)

    Google Scholar 

  234. 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)

    Article  Google Scholar 

  235. Swan, M.: The quantified self: Fundamental disruption in big data science and biological discovery. Big Data 1(2), 85–99 (2013)

    Article  Google Scholar 

  236. Huang, B.E., Mulyasasmita, W., Rajagopal, G.: The path from big data to precision medicine. Expert Rev. Precis. Med. Drug Dev. 1(2), 129–143 (2016)

    Article  Google Scholar 

  237. Bradley, P.S.: Implications of big data analytics on population health management. Big Data 1(3), 152–159 (2013)

    Article  Google Scholar 

  238. 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)

  239. Hung, C.L., Lin, Y.L.: Implementation of a parallel protein structure alignment service on cloud. Int. J. Genom., (2013)

  240. 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. 164–171. Ieee (2012)

  241. Meng, B., Pratx, G., Xing, L.: Ultrafast and scalable cone-beam ct reconstruction using mapreduce in a cloud computing environment. Med. Phys. 38(12), 6603–6609 (2011)

    Article  Google Scholar 

  242. 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), 42–47 (2014)

    Article  Google Scholar 

  243. Maia, A.T., Sammut, S.J., Jacinta-Fernandes, A., Chin, S.F.: Big data in cancer genomics. Curr. Opin. Syst. Biol. 4, 78–84 (2017)

    Article  Google Scholar 

  244. 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), 178–179 (2015)

    Article  Google Scholar 

  245. Viceconti, M., Hunter, P., Hose, R.: Big data, big knowledge: big data for personalized healthcare. IEEE J. Biomed. Health Inf. 19(4), 1209–1215 (2015)

    Article  Google Scholar 

  246. 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’s disease: the brain health modeling initiative to foster actionable knowledge. Alzheimer’s Dement. 12(9), 1014–1021 (2016)

    Article  Google Scholar 

  247. El Naqa, I.: Perspectives on making big data analytics work for oncology. Methods 111, 32–44 (2016)

    Article  Google Scholar 

  248. 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

  249. Karmonik, C., Boone, T.B., Khavari, R.: Workflow for visualization of neuroimaging data with an augmented reality device. J. Dig. Imaging 31(1), 26–31 (2018)

    Article  Google Scholar 

  250. 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–e292 (2018)

    Article  Google Scholar 

  251. Yu, D., Engel, K.: Joint visualization of 3d reconstructed photograph and internal medical scan (2018). US Patent App. 10/092,191

  252. Jorge, J.A., Simões Lopes, D.: Challenges and approaches to interactive visualization in healthcare workspaces. Ann. Med. 51(sup1), 22–22 (2019)

    Article  Google Scholar 

  253. 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, 61050–61064 (2018)

    Article  Google Scholar 

  254. 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, 1–6 (2019)

    Article  Google Scholar 

  255. 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, 374–382 (2019)

    Article  Google Scholar 

  256. Razzak, I., Naz, S., Rehman, A., Khan, A., Zaib, A.: Improving coronavirus (covid-19) diagnosis using deep transfer learning. medRxiv (2020)

  257. 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. 1, pp. 144–148. IEEE (2014)

  258. 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, 61–73 (2015)

    Article  Google Scholar 

  259. Markonis, D., Schaer, R., Eggel, I., Müller, 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. 1–1. IEEE (2012)

  260. Benjamin, M., Aradi, Y., Shreiber, R.: From shared data to sharing workflow: merging pacs and teleradiology. Eur. J. Radiol. 73(1), 3–9 (2010)

    Article  Google Scholar 

  261. Costa, C., Oliveira, J.L.: Telecardiology through ubiquitous internet services. Int. J. Med. Inf. 81(9), 612–621 (2012)

    Article  Google Scholar 

  262. Ross, P., Pohjonen, H.: Images crossing borders: image and workflow sharing on multiple levels. Insights Imaging 2(2), 141–148 (2011)

    Article  Google Scholar 

  263. 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)

  264. Zou, Q., Zeng, J., Cao, L., Ji, R.: A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing 173, 346–354 (2016)

    Article  Google Scholar 

  265. 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)

    Article  Google Scholar 

  266. Lualdi, M., Fasano, M.: Statistical analysis of proteomics data: a review on feature selection. J. Proteom. 198, 18–26 (2019)

    Article  Google Scholar 

  267. 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. 105–126. IGI Global (2019)

  268. Devi, A.S., Maragatham, G.: Big genome data classification with random forests using variantspark. In: International Conference on Computer Networks and Communication Technologies, pp. 599–614. Springer (2019)

  269. Patel, D.T.: Big data analytics in bioinformatics. In: Biotechnology: Concepts, Methodologies, Tools, and Applications, pp. 1967–1984. IGI Global (2019)

  270. 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, 75–82 (2016)

    Article  Google Scholar 

  271. 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)

    Article  Google Scholar 

  272. He, C., Fan, X., Li, Y.: Toward ubiquitous healthcare services with a novel efficient cloud platform. IEEE Trans. Biomed. Eng. 60(1), 230–234 (2012)

    Article  Google Scholar 

  273. 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. 1575–1583. Springer (2014)

  274. 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, 124–149 (2018)

    Article  Google Scholar 

  275. Wang, Y., Kung, L., Byrd, T.A.: Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Change 126, 3–13 (2018)

    Article  Google Scholar 

  276. Gupta, M., George, J.F.: Toward the development of a big data analytics capability. Inf. Manag. 53(8), 1049–1064 (2016)

    Article  Google Scholar 

  277. Wang, Y., Byrd, T.A.: Business analytics-enabled decision-making effectiveness through knowledge absorptive capacity in health care. J. Knowl. Manag. 21(3), 517–539 (2017)

    Article  Google Scholar 

  278. 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), 257–269 (2017)

    Article  Google Scholar 

  279. 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)

    Article  Google Scholar 

  280. 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), 34–40 (2013)

    Article  Google Scholar 

  281. Sobhy, D., El-Sonbaty, Y., Elnasr, M.A.: Medcloud: healthcare cloud computing system. In: 2012 International Conference for Internet Technology and Secured Transactions, pp. 161–166. IEEE (2012)

  282. Lin, W., Dou, W., Zhou, Z., Liu, C.: A cloud-based framework for home-diagnosis service over big medical data. J. Syst. Softw. 102, 192–206 (2015)

    Article  Google Scholar 

  283. 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. 121–147. Springer (2019)

  284. Garattini, C., Raffle, J., Aisyah, D.N., Sartain, F., Kozlakidis, Z.: Big data analytics, infectious diseases and associated ethical impacts. Philos. Technol. 32(1), 69–85 (2019)

    Article  Google Scholar 

  285. 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, 2008–2009. BMC Med. Inf. Decis. Mak. 14(1), 44 (2014)

    Article  Google Scholar 

  286. Cunha, J., Silva, C., Antunes, M.: Health twitter big bata management with hadoop framework. Proc. Comput. Sci. 64, 425–431 (2015)

    Article  Google Scholar 

  287. 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), 199–206 (2018)

    Article  Google Scholar 

  288. 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)

    Article  Google Scholar 

  289. 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, 86–89 (2019)

    Article  Google Scholar 

  290. 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)

    Article  Google Scholar 

  291. Conway, M., O’Connor, D.: Social media, big data, and mental health: current advances and ethical implications. Curr. Opin. Psychol. 9, 77–82 (2016)

    Article  Google Scholar 

  292. 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), 332–338 (2013)

    Article  Google Scholar 

  293. Bhardwaj, N., Wodajo, B., Spano, A., Neal, S., Coustasse, A.: The impact of big data on chronic disease management. Health Care Manag 37(1), 90–98 (2018)

    Article  Google Scholar 

  294. 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), 204–212 (2015)

    Article  Google Scholar 

  295. 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–w168 (2007)

    Article  Google Scholar 

  296. Consortium, I.H.G.S., et al.: Initial sequencing and analysis of the human genome. Nature 409(6822), 860 (2001)

  297. 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

  298. Hey, A.J., Trefethen, A.E.: The data deluge: an e-science perspective. (2003)

  299. Ritter, F., Boskamp, T., Homeyer, A., Laue, H., Schwier, M., Link, F., Peitgen, H.O.: Medical image analysis. IEEE Pulse 2(6), 60–70 (2011)

    Article  Google Scholar 

  300. D’Agostino Sr, R.B., Grundy, S., Sullivan, L.M., Wilson, P., Group, C.R.P., et al.: Validation of the framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. Jama 286(2), 180–187 (2001)

  301. Waqialla, M., Razzak, M.I.: An ontology-based framework aiming to support cardiac rehabilitation program. Proc. Comput. Sci. 96, 23–32 (2016)

    Article  Google Scholar 

  302. Alexander, C., Wang, L.: Big data analytics in heart attack prediction. J. Nurs. Care 6(393), 1168–2167 (2017)

    Google Scholar 

  303. 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. 108–115. IEEE (2008)

  304. Shamli, N., Sathiyabhama, B.: Parkinson’s brain disease prediction using big data analytics (2016)

  305. 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. 1–6. IEEE (2020)

  306. Kamran, I., Naz, S., Razzak, I., Imran, M.: Handwriting dynamics assessment using deep neural network for early identification of Parkinson’s disease. Future Gener. Comput. Syst. (2020)

  307. Sadhana, S.S., Shetty, S.: Analysis of diabetic data set using hive and r. Int. J. Emerg. Technol. Adv. Eng. 4(7), 626–9 (2014)

    Google Scholar 

  308. Daghistani, T., Al Shammari, R., Razzak, M.I.: Discovering diabetes complications: an ontology based model. Acta Inf. Med. 23(6), 385 (2015)

    Article  Google Scholar 

  309. 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. 149–152. IEEE (2017)

  310. Helm-Murtagh, S.C.: Use of big data by blue cross and blue shield of North Carolina. North Carol. Med. J. 75(3), 195–197 (2014)

    Article  Google Scholar 

  311. Wu, X., Zhu, X., Wu, G.Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Imran Razzak.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rehman, A., Naz, S. & Razzak, I. Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities. Multimedia Systems 28, 1339–1371 (2022). https://doi.org/10.1007/s00530-020-00736-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-020-00736-8

Keywords

Navigation