Skip to main content

Big Data Applications in Health Care and Education

  • Chapter
  • First Online:
  • 2048 Accesses

Part of the book series: Studies in Big Data ((SBD,volume 44))

Abstract

Technology plays a major role in all spheres of life and higher education and health care are no exceptions. The use of big data in higher education and health care are relatively new. The dynamics of higher education is passing through a phase of rapid changes. Also, the amount of data available in this field and proper analytics can reap the benefits and highlight on future techniques to be followed in handling the complex situations arisen from pressure exerted by accrediting agencies, governments and other stake holders. Higher education is becoming more and more complex with several institutes entering into the market with more and more diversified approaches. This makes the functionalities of all institutes of higher education to revise their approaches frequently to cope up with this pressure. The educational institutes have to ensure that the quality of learning programmes is at par with that of their counterparts at the national and global level. Analysis of vast data sources generated in this connection being more often not available for analysis is a major concern. The analysis of these volumes of data plays a major role in understanding and ensuring that institutions are aware of the changes occurring everywhere and they are taking care of their social responsibilities. Due to digitization of medical records in an attempt to make them available for research and development over the past ten to fifteen years, there is a huge amount of data, which besides being voluminous are complex, diverse and temporal which is collected by healthcare stockholders. An analysis of these data could collectively help the healthcare industry to find out problems related to variability in healthcare quality and escalating healthcare expenditure. In this chapter we shall make a critical analysis of these aspects of higher education and healthcare with respect to big data analysis and make some recommendations in this direction.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Daniel, B. K., & Butson, R. (2013). Technology enhanced analytics (TEA) in higher education. In Proceedings of the International Conference on Educational Technologies, 29 November–1 December, 2013, Kuala Lumpur, Malaysia (pp. 89–96).

    Google Scholar 

  2. Mauro, A. D., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. In AIP Proceedings of the International Conference on Integrated Information (IC-ININFO 2014) (Vol. 1644, pp. 97–104).

    Google Scholar 

  3. Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research, 2, 59–64.

    Article  Google Scholar 

  4. Daniel, B. (2014). Big data and analytics in higher education: Opportunities and challenges. British Journal of Education Technology, 1–17.

    Google Scholar 

  5. Huang, T., Lan, L., Fang, X., An, P., Min, J., & Wang, F. (2015). Promises and challenges of big data computing in health sciences. Big Data Research, 2, 2–11.

    Article  Google Scholar 

  6. Tulasi, B. (2013). Significance of big data and analytics in higher education. International Journal of Computer Applications, 68(14), 21–23.

    Article  Google Scholar 

  7. Sin, K., & Muthu, L. (2015). Application of big data in education data mining and learning analytics—A literature review. ICTACT Journal on Soft Computing (Special Issue on Soft Computing Models for Big Data), 1035–1049.

    Article  Google Scholar 

  8. Hilbert, M. (2014). Big data for development: From information to knowledge societies (January 15, 2013). Retrieved October 30, 2014 from http://ssrn.com/abstract=2205145 or https://doi.org/10.2139/ssrn.2205145.

  9. Kumar, V., & Chadha, A. (2011). An empirical study of data mining techniques in higher education. International Journal of Advanced Computer Science and Applications, 2(3), 80–84.

    Google Scholar 

  10. Pandey, U.K., & Pal, S. (2011). A data mining view on class room teaching language. International Journal of Computer Science and Information Technologies, 2(2), 686–690.

    Google Scholar 

  11. Pal, S. (2012). Mining educational data to reduce dropout rates of engineering students. International Journal of Information Engineering and Electronic Business, 4(2), 1.

    Article  Google Scholar 

  12. Luan, J. (2012). Data mining and its application in higher education. In A. Serban, & J. Luan (Eds.), Knowledge management: Building a competitive advantage in higher education (pp. 17–36).

    Article  Google Scholar 

  13. Michalik, P., Stofa, J., & Zolotova, I. (2014). Concept definition for big data architecture in the education system. In IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (pp. 3321–334).

    Google Scholar 

  14. Kalota, F. (2015). Applications of big data in education, world academy of science, engineering and technology. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 9(5), 1607–1611.

    Google Scholar 

  15. Romero, C. R., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 40(6), 601–618.

    Article  Google Scholar 

  16. Bresfelean, V. P. (2008). Data mining applications in higher education and academic intelligence management. In J. E. Meng, & Z. Yi (Eds.), Theory and novel applications of machine learning (pp. 209–228).

    Google Scholar 

  17. Bhardwaj, B. K., & Pal, S. (2011). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications, 2(6), 63–69.

    Google Scholar 

  18. Bhardwaj, B. K., & Pal, S. (2012). Data mining: A prediction for performance improvement using classification. International Journal of Computer Science and Information Security, 9(4).

    Google Scholar 

  19. Minaei-Bidgoli, B., Kashy, D., Kortmeyer, G., & Punch, W. (2003). Predicting student performance: An application of data mining methods with an educational web-based system. In Proceedings of 33rd Annual Frontiers in Education Conference FIE 2003 (pp. T2A13–T2A18).

    Google Scholar 

  20. Downs, E. N. (2014). UF hires bioinformatics expert. https://m.ufhealth.org/news/2014/uf-hires-bioinformatics-report.

  21. Merelli, I., Perez-Sanchez, H., Gesing, S. & D’Agostino, D. (2014). Managing, analyzing and integrating big data in medical bioinformatics: Open problems and future perspectives (pp. 1–13). Hindawi Publishing Corporation, Biomed Research International.

    Google Scholar 

  22. Feldman, B., Martin, E. M., & Skotnes, T. (2012). Big data in healthcare, hype and hope. In Dr. Bonnie, Business development for digital health (Vol. 360, pp. 1–56).

    Google Scholar 

  23. Duan, L., Street, W. N., & Xu, E. (2011). Healthcare information systems: Data mining methods in the creation of a clinical recommender system. Entrepreneurs Information Systems, 5, 169–181.

    Article  Google Scholar 

  24. Hoens, T. R., Blanton, M., Steele, A., & Chawla, N. V. (2013). Reliable medical recommendation systems with patient privacy. ACM Transactions on Intelligent Systems and Technology, 4, 1–31.

    Article  Google Scholar 

  25. Wiesner, M., & Daniel, P. (2014). Health recommender systems: Concepts requirements, technical basics and challenges. International Journal of Environmental Research and Public Health, 11(3), 2580–2607.

    Article  Google Scholar 

  26. Signorini, A., Segre, A. M., & Polgreen, P. M. (2011). The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoSONE, 6(5), e19467, 1–10. www.plosone.org.

    Article  Google Scholar 

  27. Paul, M. J., Dredze, M., & Broniatowski, D. (2014). Twitter improves influenza forecasting. PLoS Currents. www.ncbi.nlm.nih.gov.

  28. Tripathy, B.K., Chowdhury, R., & Thakur, S. (2016). A classification model to analyze the spread and emerging trends of the Zika virus in Twitter, In The proceedings of International Conference on Computational Intelligence in Data Mining (ICCIDM 2016). Advances in Intelligent Systems and Computing (AISC, Vol. 556, pp. 643–650).

    Google Scholar 

  29. Freedman, M. L., Monteiro, A. N., Gayther, S. A., et al. (2011). Principles for the post-GWAS functional characterization of cancer risk loci. Nature Genetics, 43, 513–518.

    Article  Google Scholar 

  30. Zheng, Y., Liu, F., & Hsieh, H.-P. (2013). U-Air: When urban air quality inference meets big data. In KDD’13, 11–14 August 2013, Chicago, Illinois, USA.

    Google Scholar 

  31. Mei, S., Li, H., Fan, J., Zhu, X., & Dyer C. R. (2013). Inferring air pollution by sniffing social media.

    Google Scholar 

  32. Honicky, R.J., Brewer, E. A., Paulos, E., & White, R. M. (2008). N-SMARTS: Networked suite of mobile atmospheric real-time sensors. In NSDR’08, 18 August 2008, Seattle, Washington, USA (pp. 25–29).

    Google Scholar 

  33. Chen, B. H., Hong, C. J., Pandey, M. R., & Smithd, K. R. (1990). Indoor air pollution in developing countries. World Health Statistics Quarterly, 43, 127–138.

    Google Scholar 

  34. Davis, B. (2005). Growing pains for metabolomics. The Scientist, 19(8), 25–28.

    Google Scholar 

  35. Jordan, K. W., Nordenstam, J., Lauwers, G. Y., Rothenberger, D. A., Alavi, K., Garwood, M., et al. (2009). Metabolomic characterization of human rectal adenocarcinoma with intact tissue magnetic resonance spectroscopy. Diseases of the Colon and Rectum, 52(3), 520–525.

    Article  Google Scholar 

  36. Dettmer, K., Aronov, P. A., & Hammock, B. D. (2007). Mass spectrometry-based metabolomics. Mass Spectrometry Reviews, 26(1), 51–78.

    Article  Google Scholar 

  37. Zhang, A. H., Qiu, S., Xu, H. Y., Sun, H., & Wang, X. J. (2014). Metabolomics in diabetes. Clinica Chimica Acta, 429, 106–110.

    Article  Google Scholar 

  38. Donald G., Paul, R., Watkins, B., & Michael, D. (2011). Reily: Metabolomics in toxicology: Preclinical and clinical applications. Toxicological Sciences, 120(suppl_1), S146–S170.

    Google Scholar 

  39. McKinsey and Company: How big data can revolutionize pharmaceutical R&D. http://www.mckinsey.com/insights/health_systems_and_services/how_big_data_can_revolutionize_pharmaceutical_r_and_d.

  40. Medill Reports. (2014). http://news.medill.northwestern.edu/Chicago/news.aspx?id=228875.

  41. Xian Sheng, K. (2014). Big data x-learning resources integration and processing in cloud environment. International Journal of Emerging Technologies and Learning, 9(5), 22–26.

    Google Scholar 

  42. Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Networks, 16(3), 9–20.

    Google Scholar 

  43. Romero, C. R., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 40(6), 601–618.

    Article  Google Scholar 

  44. Wagner, E., & Ice, P. (2012). Data changes everything: Delivering on the promise of learning analytics in higher education. Educause Review, 33–42.

    Google Scholar 

  45. Niemi, D., & Gitin, E. (2012). Using big data to predict student dropouts technology affordances for research. In Proceedings from the International Association for Development of the Information Society (IADIS) International Conference on Cognition and Exploratory Learning in Digital Age.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. K. Tripathy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Tripathy, B.K. (2018). Big Data Applications in Health Care and Education. In: Roy, S., Samui, P., Deo, R., Ntalampiras, S. (eds) Big Data in Engineering Applications. Studies in Big Data, vol 44. Springer, Singapore. https://doi.org/10.1007/978-981-10-8476-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8476-8_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8475-1

  • Online ISBN: 978-981-10-8476-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics