Advertisement

The Challenge of Big Data and Data Mining in Aging Research

  • Juan Carlos Gómez-VerjánEmail author
  • Luis Miguel Gutiérrez-RobledoEmail author
Chapter

Abstract

Population growth and fast evolution of several technologies into almost every corner of human activities have become a constant in the modern society nowadays. Consequently, an enormous amount of information is generated every day, from many sources, such as: web information, mobile phones, social media, scientific reports, medical and healthcare information among others, resulting in the so called Big Data. Digitization, storage, collection, and particularly analysis of patterns on these data have led to advances in data sciences (Data Mining) to obtain valuable information from people necessities, demands, hobbies, and activities daily from such databases (Big Knowledge). On the other side, aging has become one of the main challenges of modern societies, since there is an increase on prevalence of chronic pathological conditions and aging associated outcomes. Biomedical and clinical research have experienced several advances in the last years with the implementation of omics technologies and digitization of healthcare, in this context, Big Data and Data Mining become a powerful tool to obtain valuable information that could be used to approach the complexity of the aging process and the clinical implications for older adults. Several companies and governments all over the world have already started to successfully implement these types of technologies from pharmaceutical developments to public health issues. Although future seems promising novel holistic approaches are needed to implement a multidisciplinary agenda that could help us understand the complexities of human aging and develop the appropriate interventions to improve older people’s health in healthcare systems.

Keywords

Big data Data mining Big knowledge Systems biology Public health 

References

  1. 1.
    Yang C-T, Liu J-C, Chen S-T, Lu H-W (2017) Implementation of a big data accessing and processing platform for medical records in cloud. J Med Syst 41:149.  https://doi.org/10.1007/s10916-017-0777-5 CrossRefPubMedGoogle Scholar
  2. 2.
    Data Never Sleeps 5.0 | Domo [Internet]. [cited 29 Nov 2017]. Available: https://www.domo.com/learn/data-never-sleeps-5
  3. 3.
    Vitari C, Raguseo E (2016) Digital data, dynamic capability and financial performance: an empirical investigation in the era of big data. Systèmes d’Information & Management 21(3):6392.  https://doi.org/10.3917/sim.163.0063 CrossRefGoogle Scholar
  4. 4.
    Han J, Kamber M, Pei J (2012) Data mining trends and research frontiers. Data Min:585–631Google Scholar
  5. 5.
    Piatetsky-Shapiro G (1994) An overview of knowledge discovery in databases: recent progress and challenges. Workshops in computing pp 1–10Google Scholar
  6. 6.
    Maimon O, Rokach L (2009) Introduction to knowledge discovery and data mining. In: Data mining and knowledge discovery handbook, pp 1–15Google Scholar
  7. 7.
    McCue C (2015) Chapter 3 - data mining and predictive analytics. In: McCue C (ed) Data mining and predictive analysis, 2nd edn. Butterworth-Heinemann, Boston, pp 31–48CrossRefGoogle Scholar
  8. 8.
    Freitas A, Curry E (2016) Big data curation. In: New horizons for a data-driven economy, pp 87–118CrossRefGoogle Scholar
  9. 9.
    Kononenko I, Kukar M (2007) Chapter 3 - machine learning basics. In: KIK M (ed) Machine learning and data mining. Woodhead Publishing, pp 59–105Google Scholar
  10. 10.
    Michael A, AGSL B (2008) Mastering data mining: the art and science of customer relationship management. Ind Manag Data Syst 100(5):245–246Google Scholar
  11. 11.
    McCue C (2015) Process models for data mining and predictive analysis. In: Data mining and predictive analysis, pp 51–74CrossRefGoogle Scholar
  12. 12.
    Vayena E, Blasimme A (2017) Biomedical big data: new models of control over access, use and governance. J Bioeth Inq 14(4):501–513.  https://doi.org/10.1007/s11673-017-9809-6 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Ruckenstein M, Schüll ND (2017) The datafication of health. Annu Rev Anthropol 46:261–278CrossRefGoogle Scholar
  14. 14.
    Vayena E, Dzenowagis J, Brownstein JS, Sheikh A (2017) Policy implications of big data in the health sector. Bull World Health Organ 96(1):66–68.  https://doi.org/10.2471/BLT.17.197426 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Cabitza F, Rasoini R, Gensini GF (2017) Unintended consequences of machine learning in medicine. JAMA 318(6):517–518.  https://doi.org/10.1001/jama.2017.7797 CrossRefPubMedGoogle Scholar
  16. 16.
    Bui AAT, Van Horn JD (2017) NIH BD2K centers consortium. Envisioning the future of “big data” biomedicine. J Biomed Inform 69:115–117.  https://doi.org/10.1016/j.jbi.2017.03.017 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Kolitsi Z, Thonnet M (2014) New directions in eHealth governance in Europe. In: Managing eHealth. Palgrave Macmillan, London, pp 50–60Google Scholar
  18. 18.
    Salerno J, Knoppers BM, Lee LM, Hlaing WM, Goodman KW (2017) Ethics, big data and computing in epidemiology and public health. Ann Epidemiol 27(5):297–301.  https://doi.org/10.1016/j.annepidem.2017.05.002 CrossRefPubMedGoogle Scholar
  19. 19.
    Johanson P-E, Fenstad AM, Furnes O, Garellick G, Havelin LI, Overgaard S et al (2010) Inferior outcome after hip resurfacing arthroplasty than after conventional arthroplasty. Evidence from the nordic arthroplasty register association (NARA) database, 1995 to 2007. Acta Orthop 81(5):535–541.  https://doi.org/10.3109/17453674.2010.525193 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Stang PE, Ryan PB, Racoosin JA, Marc Overhage J, Hartzema AG, Reich C et al (2010) Advancing the science for active surveillance: rationale and design for the observational medical outcomes partnership. Ann Intern Med 153(9):600–606.  https://doi.org/10.7326/0003-4819-153-9-201011020-00010 CrossRefPubMedGoogle Scholar
  21. 21.
    Jung JJ, Kim P (2017) Big data technologies and applications: 7th international conference, BDTA 2016, Seoul, South Korea, 17–18 Nov, 2016, Proceedings. SpringerGoogle Scholar
  22. 22.
    Yoo S, Hwang H, Jheon S (2016) Hospital information systems: experience at the fully digitized Seoul National University Bundang hospital. J Thorac Dis 8(Suppl 8):S637–S641.  https://doi.org/10.21037/jtd.2016.08.44 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    He KY, Ge D, He MM (2017) Big data analytics for genomic medicine. Int J Mol Sci 18(2):1–18.  https://doi.org/10.3390/ijms18020412 CrossRefGoogle Scholar
  24. 24.
    Chervitz SA, Deutsch EW, Field D, Parkinson H, Quackenbush J, Rocca-Serra P et al (2011) Data standards for omics data: the basis of data sharing and reuse. Methods Mol Biol 719:31–69.  https://doi.org/10.1007/978-1-61779-027-0_2 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    OECD (2017) OECD average life expectancy and perceived health, since 2005 [internet].  https://doi.org/10.1787/how_life-2017-graph10-en
  26. 26.
    Song P, Chen Y (2015) Public policy response, aging in place, and big data platforms: creating an effective collaborative system to cope with aging of the population. Biosci Trends 9(1):1–6.  https://doi.org/10.5582/bst.2015.01025 CrossRefPubMedGoogle Scholar
  27. 27.
    Kwon Y, Natori Y, Tanokura M (2017) New approach to generating insights for aging research based on literature mining and knowledge integration. PLoS One 12(8):e0183534.  https://doi.org/10.1371/journal.pone.0183534 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Callaghan CW (2017) Developing the transdisciplinary aging research agenda: new developments in big data. Curr Aging Sci 10.  https://doi.org/10.2174/1874609810666170719100122 CrossRefGoogle Scholar
  29. 29.
    Crawford K, Finn M (2014) The limits of crisis data: analytical and ethical challenges of using social and mobile data to understand disasters. GeoJournal 80:491–502.  https://doi.org/10.1007/s10708-014-9597-z CrossRefGoogle Scholar
  30. 30.
    Tamiya N, Noguchi H, Nishi A, Reich MR, Ikegami N, Hashimoto H et al (2011) Population ageing and wellbeing: lessons from Japan’s long-term care insurance policy. Lancet 378(9797):1183–1192.  https://doi.org/10.1016/S0140-6736(11)61176-8 CrossRefPubMedGoogle Scholar
  31. 31.
    Hansen DP, Gurney P, Morgan G, Barraclough B (2011) The Australian e-Health research centre: enabling the health care information and communication technology revolution. Med J Aust 194(4):S5–S7PubMedGoogle Scholar
  32. 32.
    Sonnega A, Robinson K, Levy H (2016) Home and community-based service and other senior service use: prevalence and characteristics in a national sample. Home Health Care Serv Q 36(1):16–28.  https://doi.org/10.1080/01621424.2016.1268552 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Lorusso JS, Sviderskiy OA, Labunskyy VM (2017) Emerging omics approaches in aging research. Antioxid Redox Signal.  https://doi.org/10.1089/ars.2017.7163 CrossRefGoogle Scholar
  34. 34.
    Lund E, Dumeaux V (2008) Systems epidemiology in cancer. Cancer Epidemiol Biomark Prev 17(11):2954–2957.  https://doi.org/10.1158/1055-9965.EPI-08-0519 CrossRefGoogle Scholar
  35. 35.
    Sidorenko AV, Mikhailova ON (2013) Implementation of the Madrid international plan of action on ageing in the CIS countries: the first 10 years. Adv Gerontol 26(4):585–593PubMedGoogle Scholar
  36. 36.
    de Magalhães JP, Stevens M, Thornton D (2017) The business of anti-aging science. Trends Biotechnol 35(11):1062–1073.  https://doi.org/10.1016/j.tibtech.2017.07.004 CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Basic ResearchNational Institute of GeriatricsMexico CityMexico
  2. 2.National Institute of GeriatricsMexico CityMexico

Personalised recommendations