Advertisement

Role and Challenges of Unstructured Big Data in Healthcare

  • Kiran AdnanEmail author
  • Rehan Akbar
  • Siak Wang Khor
  • Adnan Bin Amanat Ali
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1042)

Abstract

Unprecedented growth in the volume of unstructured healthcare data has immense potential in valuable insight extraction, improved healthcare services, quality patient care, and secure data management. However, technological advancements are required to achieve the potential benefits from unstructured data in healthcare according to the growth rate. The heterogeneity, diversity of sources, quality of data and various representations of unstructured data in healthcare increases the number of challenges as compared to structured data. This systematic review of the literature identifies the challenges and problems of data-driven healthcare due to the unstructured nature of data. The systematic review was carried out using five major scientific databases: ACM, Springer, ScienceDirect, PubMed, and IEEE Xplore. The inclusion of articles in review at the initial stage was based on English language and publication date from 2010 to 2018. A total of 103 articles were selected according to the inclusion criteria. Based on the review, various types of healthcare unstructured data have been discussed from different domains of healthcare. Also, potential challenges associated with unstructured big data have been identified in healthcare for future research directions in the technological advancement of healthcare services and quality patient care.

Keywords

Unstructured data Healthcare Big data Systematic literature review Challenges of healthcare data 

Notes

Acknowledgements

This research is funded by Universiti Tunku Abdul Rahman (UTAR) under the UTAR Research Fund (UTARRF): IPSR/RMC/UTARRF/2017-C1/R02.

References

  1. 1.
    Gantz, J., Reinsel, D.: The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. www.emc.com/collateral/analyst-reports/idc-the-digital-universein-2020.pdf (2012)
  2. 2.
    Turner, V., Gantz, J.F., Reinsel, D., Minton, S.: The digital universe of opportunities: rich data and increasing value of the internet of things. IDC White Paper, No. April, pp. 1–5 (2014)Google Scholar
  3. 3.
    Kiran, A., Vasumathi, D.: Predictive methodology for women health analysis through social media. In: Proceedings of the Second International Conference on Computational Intelligence and Informatics, vol. 712, Springer Singapore, pp. 511–520 (2018)CrossRefGoogle Scholar
  4. 4.
    Correia, C., Portela, F., Santos, M.F., Silva, Á.: Data science analysis of healthcare complaints. In: Trends and Advances in Information Systems and Technologies, vol. 747, Springer International Publishing, pp. 176–185 (2018)Google Scholar
  5. 5.
    Kim, K.H., et al.: A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: a preliminary study. Korean Phys. Soc. J. 71(4), 231–237 (2017)CrossRefGoogle Scholar
  6. 6.
    Anzaldi, L.J., Davison, A., Boyd, C.M., Leff, B., Kharrazi, H.: Comparing clinician descriptions of frailty and geriatric syndromes using electronic health records: a retrospective cohort study. BMC Geriatr. 17(1), 1–7 (2017)CrossRefGoogle Scholar
  7. 7.
    Saiod, A.K., Van Greunen, D., Veldsman, A.: Electronic health records: benefits and challenges for data quality. In: Handbook of Large-Scale Distributed Computing in Smart Healthcare, Springer, Cham, pp. 123–156 (2017)CrossRefGoogle Scholar
  8. 8.
    Gökalp, M.O., Kayabay, K., Akyol, M.A., Koçyiğit, A., Eren, P.E.: Big Data in mHealth. In: Current and emerging mHealth technologies, Springer International Publishing, pp. 241–256 (2018)Google Scholar
  9. 9.
    Austin, C., Kusumoto, F.: The application of Big Data in medicine: current implications and future directions. Interv. Card. Electrophysiol. 47(1), 51–59 (2016)CrossRefGoogle Scholar
  10. 10.
    Angelov, P., Sadeghi-Tehran, P.: A nested hierarchy of dynamically evolving clouds for big data structuring and searching. Procedia Comput. Sci. 53(1), 1–8 (2015)CrossRefGoogle Scholar
  11. 11.
    Kundeti, S.R., Vijayananda, J.: Clinical named entity recognition: challenges and opportunities. In: IEEE International Conference on Big Data (Big Data), pp. 1937–1945 (2016)Google Scholar
  12. 12.
    Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Heal. Inf. Sci. Syst. 2(1), 3 (2014)CrossRefGoogle Scholar
  13. 13.
    Liu, M., Hu, Y., Tang, B.: Role of Text Mining in Early Identification of Potential Drug Safety Issues, pp. 227–251. Humana Press, New York, NY (2014)Google Scholar
  14. 14.
    Luo, L., et al.: A hybrid solution for extracting structured medical information from unstructured data in medical records via a double-reading/entry system. BMC Med. Inform. Decis. Mak. 16(1), 1–14 (2016)CrossRefGoogle Scholar
  15. 15.
    van Ooijen, P.M., Jorritsma, W.: Medical imaging informatics in nuclear medicine. In: Quality in Nuclear Medicine. Springer, Cham, pp. 241–267 (2017)Google Scholar
  16. 16.
    Saravana Kumar, N.M., Eswari, T., Sampath, P., Lavanya, S.: Predictive methodology for diabetic data analysis in big data. Procedia Comput. Sci. 50, 203–208 (2015)CrossRefGoogle Scholar
  17. 17.
    Marashi, P.S., Hamidi, H.: Business challenges of big data application in health organization. In: Competitiveness in Emerging Markets. Springer, Cham, pp. 569–584 (2018)Google Scholar
  18. 18.
    Bandyopadhyay, S., et al.: Modeling heterogeneous clinical sequence data in semantic space for adverse drug event detection. In: Data Mining and Knowledge Discovery (2015), p. 31 (2015)Google Scholar
  19. 19.
    Ling, Z.J., et al.: GEMINI: an integrative healthcare analytics system. Proc. VLDB Endow. 7(13), 1766–1771 (2014)CrossRefGoogle Scholar
  20. 20.
    Wang, Y., Kung, L.A., Byrd, T.A.: Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Change 126, 3–13 (2018)CrossRefGoogle Scholar
  21. 21.
    Schmidt, D., Budde, K., Sonntag, D., Profitlich, H.J., Ihle, M., Staeck, O.: A novel tool for the identification of correlations in medical data by faceted search. Comput. Biol. Med. 85, 98–105 (2017)CrossRefGoogle Scholar
  22. 22.
    Ong, K.L., De Silva, D., Boo, Y.L., Lim, E.H., Bodi, F., Alahakoon, D., Leao, S.: Big data applications in engineering and science. In: Big Data Concepts, Theories, and Applications. Springer, Cham, pp. 315–351 (2016)CrossRefGoogle Scholar
  23. 23.
    Sedghi, E., Weber, J.H., Thomo, A., Bibok, M., Penn, A.M.: A new approach to distinguish migraine from stroke by mining structured and unstructured clinical data-sources. Netw. Model. Anal. Heal. Inf. Bioinf. 5(1), 30 (2016)Google Scholar
  24. 24.
    Apache SparkTM—Unified Analytics Engine for Big Data (online). https://spark.apache.org/. Accessed 09 Oct 2018
  25. 25.
    Apache Hadoop (online). http://hadoop.apache.org/. Accessed 09 Oct 2018
  26. 26.
    Apache Flink: Stateful Computations over Data Streams (online). https://flink.apache.org/. Accessed 09 Oct 2018
  27. 27.
    Gomathi, S., Narayani, V.: Implementing big data analytics to predict systemic lupus erythematosus. In: IEEE Sponsored 2nd International Conference on Innovations in Information, Embedded and Communication systems (ICIIECS), pp. 1–5 (2015)Google Scholar
  28. 28.
    Wu, S.T., et al.: Generality and reuse in a common type system for clinical natural language processing. In: Proceedings of the First International Workshop on Managing Interoperability and Complexity in Health Systems—MIXHS’11, p. 27 (2011)Google Scholar
  29. 29.
    Scheurwegs, E., Luyckx, K., Luyten, L., Daelemans, W., Van den Bulcke, T.: Data integration of structured and unstructured sources for assigning clinical codes to patient stays. J. Am. Med. Informatics Assoc. 23(e1), 11–19 (2016)CrossRefGoogle Scholar
  30. 30.
    Talukder, A.K.: Big data analytics advances in health intelligence, public health, and evidence-based precision medicine. Int. Conf. Big Data Anal. 10721, 243–253 (2017)CrossRefGoogle Scholar
  31. 31.
    Feldman, K., Johnson, R.A., Chawla, N.V.: The state of data in healthcare: path towards standardization. J. Healthc. Inf. Res. 2(3), 248–271 (2018)CrossRefGoogle Scholar
  32. 32.
    Yu, W.D., Kollipara, M., Penmetsa, R., Elliadka, S.: A distributed storage solution for cloud based e-Healthcare Information System. In: IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013), pp. 476–480 (2013)Google Scholar
  33. 33.
    Bhaskaran, S., Suryanarayana, G., Basu, A., Joseph, R.: Cloud-enabled search for disparate healthcare data: A case study. In: 2013 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2013, pp. 1–8 (2013)Google Scholar
  34. 34.
    Kraus, J.M., et al.: Big data and precision medicine: challenges and strategies with healthcare data. J. Int. Data Sci. Anal. J. 6(3), 1–9 (2018)CrossRefGoogle Scholar
  35. 35.
    Genannt Halfmann, S.S., Mählmann, L., Leyens, L., Reumann, M., Brand, A.: Personalized medicine: What’s in it for rare diseases? In: Rare Diseases Epidemiology: Update and Overview, Springer, Cham, pp. 387–404 (2017)Google Scholar
  36. 36.
    Istephan, S., Siadat, M.R.: Unstructured medical image query using big data—an epilepsy case study. J. Biomed. Inform. 59, 218–226 (2016)CrossRefGoogle Scholar
  37. 37.
    Auffray, C., et al.: Making sense of big data in health research: towards an EU action plan. Genome Med. 8(1), 1–13 (2016)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Cuggia, M., Avillach, P., Daniel, C.: Representation of patient data in health information systems and electronic health records. In: Medical Informatics, e-Health, pp. 65–89 (2014)Google Scholar
  39. 39.
    Cruz-Ramos, N.A., Alor-Hernández, G., Sánchez-Cervantes, J.L., Paredes-Valverde, M.A., del Pilar Salas-Zárate, M.: DiabSoft: a system for diabetes prevention, monitoring, and treatment. In: Exploring Intelligent Decision Support Systems, Springer, Cham, pp. 135–154 (2018)CrossRefGoogle Scholar
  40. 40.
    Chen, E.S., Sarkar, I.N.: Mining the electronic health record for disease knowledge. In: Biomedical Literature Mining, pp. 269–286 (2014)Google Scholar
  41. 41.
    Wu, S.T., et al.: A common type system for clinical natural language processing. J. Biomed. Semant. 4(1), 1–12 (2013)CrossRefGoogle Scholar
  42. 42.
    da Costa, C.A., Pasluosta, C.F., Eskofier, B., da Silva, D.B., da Rosa Righi, R.: Internet of Health Things: toward intelligent vital signs monitoring in hospital wards. Artif. Intell. Med. 89, 61–69 (2018)CrossRefGoogle Scholar
  43. 43.
    Kozák, J., Nečaský, M., Dědek, J.: Linked open data for healthcare professionals. In: Proceedings of International Conference on Information Integration and Web-based Applications and Services, p. 400 (2013)Google Scholar
  44. 44.
    Ilyasova, N., Kupriyanov, A., Paringer, R., Kirsh, D.: Particular use of BIG DATA in medical diagnostic tasks. Pattern Recognit. Image Anal. 28(1), 114–121 (2018)CrossRefGoogle Scholar
  45. 45.
    Goh, W.P., Tao, X., Zhang, J., Yong, J.: Decision support systems for adoption in dental clinics: a survey. Knowl. Based Syst. 104, 195–206 (2016)CrossRefGoogle Scholar
  46. 46.
    Leyens, L., Reumann, M., Malats, N., Brand, A.: Use of big data for drug development and for public and personal health and care. Genet. Epidemiol. 41(1), 51–60 (2017)CrossRefGoogle Scholar
  47. 47.
    Malmasi, S., Hosomura, N., Chang, L.-S., Brown, C.J., Skentzos, S., Turchin, A.: Extracting healthcare quality information from unstructured data. In: AMIA… Annual Symposium Proceedings/AMIA Symposium, pp. 1243–1252 (2017)Google Scholar
  48. 48.
    Martínez, P., Martínez, J.L., Segura-Bedmar, I., Moreno-Schneider, J., Luna, A., Revert, R.: Turning user generated health-related content into actionable knowledge through text analytics services. Comput. Ind. 78, 43–56 (2016)CrossRefGoogle Scholar
  49. 49.
    Sundararaman, A., Valady Ramanathan, S., Thati, R.: Novel approach to predict hospital readmissions using feature selection from unstructured data with class imbalance. Big Data Res. 1, 1–11 (2018)Google Scholar
  50. 50.
    Delespierre, T., Denormandie, P., Bar-Hen, A., Josseran, L.: Empirical advances with text mining of electronic health records. BMC Med. Inform. Decis. Mak. 17(1), 1–15 (2017)CrossRefGoogle Scholar
  51. 51.
    Wilcox, A.B.: Leveraging electronic health records for phenotyping. In: Translational Informatics. Springer, London, pp. 61–74 (2015)Google Scholar
  52. 52.
    Simmons, M., Singhal, A., Lu, Z.: Text mining for precision medicine: bringing structure to EHRs and biomedical literature to understand genes and health. In: Translational Biomedical Informatics, vol. 939, pp. 139–166 (2016)Google Scholar
  53. 53.
    Goodman, K., Krueger, J., Crowley, J.: The automatic clinical trial: leveraging the electronic medical record in multisite cancer clinical trials. Curr. Oncol. Rep. 14(6), 502–508 (2012)CrossRefGoogle Scholar
  54. 54.
    Kotfila, C., Uzuner, Ö.: A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases. J. Biomed. Inform. 58, S92–S102 (2015)CrossRefGoogle Scholar
  55. 55.
    Alnashwan, R., Sorensen, H., O’Riordan, A., Hoare, C.: Accurate classification of socially generated medical discourse. J. Int. Data Sci. Anal., pp. 1–13 (2018)Google Scholar
  56. 56.
    Husain, S.S., Kalinin, A., Truong, A., Dinov, I.D.: SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information. J. Big Data 2(1), 13 (2015)CrossRefGoogle Scholar
  57. 57.
    Jackson, R., et al.: CogStack-experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust Hospital. BMC Med. Inf. Decis. 18(1), 47 (2018)CrossRefGoogle Scholar
  58. 58.
    Dinov, I.D.: Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data. Gigascience 5(1), 1–15 (2016)CrossRefGoogle Scholar
  59. 59.
    Hu, B.V., Terrazas, B.: Building a mental health knowledge model to facilitate decision support. In: Knowledge Management and Acquisition for Intelligent Systems, vol. 9806, pp. 198–212. Springer, Cham (2016)CrossRefGoogle Scholar
  60. 60.
    Pulmano, C.E., Estuar, M.R.J.E.: Towards developing an intelligent agent to assist in patient diagnosis using neural networks on unstructured patient clinical notes: inaccurate classification of socially generated medical discourseitial analysis and models. Procedia Comput. Sci. 100, 263–270 (2016)CrossRefGoogle Scholar
  61. 61.
    Norman, B., Davis, T., Quinn, S., Massey, R., Hirsh, D.: Automated identification of pediatric appendicitis score in emergency department notes using natural language processing. In: 2017 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 481–484 (2017)Google Scholar
  62. 62.
    Chapman, W.W., Gundlapalli, A.V., South, B.R., Dowling, J.N.: Natural language processing for biosurveillance. In: Infectious Disease Informatics and Biosurveillance, vol. 27, pp. 279–310 (2011)Google Scholar
  63. 63.
    Jonnalagadda, S.R., Adupa, A.K., Garg, R.P., Corona-Cox, J., Shah, S.J.: Text mining of the electronic health record: An information extraction approach for automated identification and subphenotyping of HFpEF patients for clinical trials. J. Cardiovasc. Transl. Res. 10(3), 313–321 (2017)CrossRefGoogle Scholar
  64. 64.
    Kim, J.C., Chung, K.: Associative feature information extraction using text mining from health big data. Wirel. Pers. Commun. 105(2), 691–707 (2018)CrossRefGoogle Scholar
  65. 65.
    Clark, A., Ng, J.Q., Morlet, N., Semmens, J.B.: Big data and ophthalmic research. Surv. Ophthalmol. 61(4), 443–465 (2016)CrossRefGoogle Scholar
  66. 66.
    Syomov, I.I., Bologva, E.V., Kovalchuk, S.V., Krikunov, A.V., Moiseeva, O.M., Simakova, M.A.: Towards infrastructure for knowledge-based decision support in clinical practice. Procedia Comput. Sci. 100, 907–914 (2016)CrossRefGoogle Scholar
  67. 67.
    Sakr, S., Elgammal, A.: Towards a comprehensive data analytics framework for smart healthcare services. Big Data Res. 4, 44–58 (2016)CrossRefGoogle Scholar
  68. 68.
    Lee, C., Murata, S., Ishigaki, K., Date, S.: A data analytics pipeline for smart healthcare applications. In: Sustained Simulation Performance 2017. Springer International Publishing, pp. 181–192 (2017)Google Scholar
  69. 69.
    Pramanik, M.I., Lau, R.Y.K., Demirkan, H., Azad, M.A.K.: Smart health: big data enabled health paradigm within smart cities. Expert Syst. Appl. 87, 370–383 (2017)CrossRefGoogle Scholar
  70. 70.
    Henriksson, A., Zhao, J., Dalianis, H., Boström, H.: Ensembles of randomized trees using diverse distributed representations of clinical events. BMC Med. Inform. Decis. Mak. 16(Suppl 2), 69–79 (2016)Google Scholar
  71. 71.
    Hochheiser, H., Castine, M., Harris, D., Savova, G., Jacobson, R.S.: An information model for computable cancer phenotypes. BMC Med. Inform. Decis. Mak. 16(1), 1–15 (2016)CrossRefGoogle Scholar
  72. 72.
    Wang, Y., et al.: NLP based congestive heart failure case finding: a prospective analysis on statewide electronic medical records. Int. J. Med. Inf. 84(12), 1039–1047 (2015)CrossRefGoogle Scholar
  73. 73.
    Jackson, K.L., et al.: Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies. BMC Infect. Dis. 16(1), 1–7 (2016)Google Scholar
  74. 74.
    Lovis, C., Gamzu, R.: Big Data in Israeli healthcare: hopes and challenges report of an international workshop. Isr. J. Health Policy Res. 4(1), 4–9 (2015)CrossRefGoogle Scholar
  75. 75.
    Jonnagaddala, J., Liaw, S.T., Ray, P., Kumar, M., Chang, N.W., Dai, H.J.: Coronary artery disease risk assessment from unstructured electronic health records using text mining. J. Biomed. Inform. 58, S203–S210 (2015)CrossRefGoogle Scholar
  76. 76.
    Bamwal, A.K., Choudhary, G.K., Swamim, R., Kedia, A., Goswami, S., Das, A.K.: Application of twitter in health care sector for India. 2016 3rd International Conference on Recent Advanced Information Technology, pp. 172–176 (2016)Google Scholar
  77. 77.
    Rinaldi, G.: An introduction to the technological basis of eHealth. In: eHealth, Care and Quality of Life. Springer Milan, pp. 31–67 (2014)Google Scholar
  78. 78.
    Persico, V.: Big data for health. In: Encyclopedia of Big Data Technologies. Springer International Publishing, pp. 1–10 (2018)Google Scholar
  79. 79.
    Grover, P., Kar, A.K., Davies, G.: ‘Technology enabled Health’—Insights from twitter analytics with a socio-technical perspective. Int. J. Inf. Manage. 43(May), 85–97 (2018)CrossRefGoogle Scholar
  80. 80.
    Metsker, O., Bolgova, E., Yakovlev, A., Funkner, A., Kovalchuk, S.: Pattern-based mining in electronic health records for complex clinical process analysis. Procedia Comput. Sci. 2017(119), 197–206 (2017)CrossRefGoogle Scholar
  81. 81.
    Khatri, I., Shrivastava, V.K.: A survey of big data in healthcare industry. Adv. Comput. Commun. Technol. 452, 245–257 (2016)CrossRefGoogle Scholar
  82. 82.
    Sarkar, B.K.: Big data for secure healthcare system: a conceptual design. Complex Intell. Syst. 3(2), 133–151 (2017)CrossRefGoogle Scholar
  83. 83.
    Wei, F., et al.: Visual content correlation analysis. In: Proceedings of the first International Workshop on Intelligence Visual Interfaces for Text Analysis—IVITA’10, no. 1, p. 25 (2010)Google Scholar
  84. 84.
    Jayalatchumy, D., Thambidurai, P.: Prediction of diseases using Hadoop in big data—a modified approach. In: Artificial Intelligence Trends in Intelligent Systems. Springer, Cham, pp. 229–238 (2017)Google Scholar
  85. 85.
    Buchan, K., Filannino, M., Uzuner, Ö.: Automatic prediction of coronary artery disease from clinical narratives. J. Biomed. Inform. 72, 23–32 (2017)CrossRefGoogle Scholar
  86. 86.
    Devarakonda, M.V., Mehta, N.: Cognitive computing for electronic medical records. In: Healthcare Information Management Systems, pp. 555–577 (2016)Google Scholar
  87. 87.
    Wang, Y., Kung, L.A., Wang, W.Y.C., Cegielski, C.G.: An integrated big data analytics-enabled transformation model: application to health care. Inf. Manag. 55(1), 64–79 (2018)CrossRefGoogle Scholar
  88. 88.
    Maitra, A., Annervaz, K.M., Jain, T.G., Shivaram, M., Sengupta, S.: A novel text analysis platform for pharmacovigilance of clinical drugs. Procedia Comput. Sci. 36, 322–327 (2014)CrossRefGoogle Scholar
  89. 89.
    Fong, A., Hettinger, A.Z., Ratwani, R.M.: Exploring methods for identifying related patient safety events using structured and unstructured data. J. Biomed. Inform. 58, 89–95 (2015)CrossRefGoogle Scholar
  90. 90.
    Singh, N., Singh, S.: Object classification to analyze medical imaging data using deep learning. In: 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1–4 (2017)Google Scholar
  91. 91.
    Batini, C., Cappiello, C., Francalanci, C., Maurino, A.: Methodologies for data quality assessment and improvement. ACM Comput. Surv. 41(3), 1–52 (2009)CrossRefGoogle Scholar
  92. 92.
    Wahyudi, A., Kuk, G., Janssen, M.: A process pattern model for tackling and improving big data quality. Inf. Syst. Front. 20(3), 457–469 (2018)CrossRefGoogle Scholar
  93. 93.
    Ullah, F., Edwards, M., Ramdhany, R., Chitchyan, R., Babar, M.A., Rashid, A.: Data exfiltration: a review of external attack vectors and countermeasures. J. Netw. Comput. Appl. 101, 18–54 (2018)CrossRefGoogle Scholar
  94. 94.
    Wuyts, K., Verhenneman, G., Scandariato, R., Joosen, W., Dumortier, J.: What electronic health records don’t know just yet. A privacy analysis for patient communities and health records interaction. Health Technol. (Berl) 2(3), 159–183 (2012)CrossRefGoogle Scholar
  95. 95.
    Istephan, M.R., Siadat, S.: Extensible query framework for unstructured medical data—a big data approach. In: IEEE International Conference on Data Mining Workshop (ICDMW), pp. 455–462 (2016)Google Scholar
  96. 96.
    Tchagna Kouanou, A., Tchiotsop, D., Kengne, R., Zephirin, D.T., Adele Armele, N.M., Tchinda, R.: An optimal big data workflow for biomedical image analysis. Inf. Med. Unlocked 11, 68–74 (2018)CrossRefGoogle Scholar
  97. 97.
    Meystre, S.M.: De-identification of unstructured clinical data for patient privacy protection. In: Medical Data Privacy Handbook. Springer, Cham, pp. 697–716 (2015)CrossRefGoogle Scholar
  98. 98.
    Aqeel-ur-Rehman, Khan, I.U., ur Sadiq ur Rehman, S.: A review on big data security and privacy in healthcare applications. In: Big Data Management. Springer International Publishing, Cham, pp. 71–89 (2017)Google Scholar
  99. 99.
    Gaylis, F., Cohen, E., Calabrese, R., Prime, H., Dato, P., Kane, C.J.: Active surveillance of prostate cancer in a community practice: how to measure, manage, and improve? Urology 93, 60–66 (2016)CrossRefGoogle Scholar
  100. 100.
    Hardy, L.R., Bourne, P.E.: Data science: transformation of research and scholarship. In: Big Data-Enabled Nursing. Springer, Cham, pp. 183–209 (2017)CrossRefGoogle Scholar
  101. 101.
    Khennou, F., Khamlichi, Y.I., El Houda Chaoui, N.: Designing a health data management system based hadoop-agent. In: 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 71–76 (2016)Google Scholar
  102. 102.
    Vest, J.R., Grannis, S.J., Haut, D.P., Halverson, P.K., Menachemi, N.: Using structured and unstructured data to identify patients’ need for services that address the social determinants of health. Int. J. Med. Inf. 107(August), 101–106 (2017)CrossRefGoogle Scholar
  103. 103.
    Hong, N., et al.: Integrating structured and unstructured EHR data using an FHIR-based type system: a case study with medication data. AMIA Joint Summits on Translational Science Proceedings, vol. 2017, pp. 74–83 (2018)Google Scholar
  104. 104.
    Rastegar-Mojarad, M., et al.: Using unstructured data to identify readmitted patients. In: IEEE International Conference on Healthcare Informatics (ICHI), pp. 1–4 (2017)Google Scholar
  105. 105.
    Boursalie, O., Samavi, R., Doyle, T.E.: Machine learning and mobile health monitoring platforms: a case study on research and implementation challenges. J. Healthc. Inf. Res. 2(1–2), 179–203 (2018)CrossRefGoogle Scholar
  106. 106.
    Zillner, S., Neururer, S.: Technology roadmap development for big data healthcare applications. KI Künstliche Intelligenz 29(2), 131–141 (2015)CrossRefGoogle Scholar
  107. 107.
    Giambrone, G.P., Hemmings, H.C., Sturm, M., Fleischut, P.M.: Information technology innovation: the power and perils of big data. Br. J. Anaesth. 115(3), 339–342 (2015)CrossRefGoogle Scholar
  108. 108.
    Banos, O., et al.: An innovative platform for person-centric health and wellness support. Int. Conf. Bioinf. Biomed. Eng. 9044, 31–140 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Kiran Adnan
    • 1
    Email author
  • Rehan Akbar
    • 1
  • Siak Wang Khor
    • 1
  • Adnan Bin Amanat Ali
    • 1
  1. 1.Faculty of Information and Communication TechnologyUniversiti Tunku Abdul RahmanKamparMalaysia

Personalised recommendations