Advertisement

Mobile Networks and Applications

, Volume 23, Issue 4, pp 1123–1128 | Cite as

A Deep-Big Data Approach to Health Care in the AI Age

  • José Neves
  • Henrique Vicente
  • Marisa Esteves
  • Filipa Ferraz
  • António Abelha
  • José Machado
  • Joana Machado
  • João Neves
  • Jorge Ribeiro
  • Lúzia Sampaio
Article
  • 191 Downloads

Abstract

The intersection of these two trends is what we call The Issue and it is helping businesses in every industry to become more efficient and productive. One’s aim is to have an insight into the development and maintenance of comprehensive and integrated health information systems that enable sound policy and effective health system management in order to improve health and health care. Undeniably, different sorts of technologies have been developed, each with their own advantages and disadvantages, which will be sorted out by attending at the impact that Artificial Intelligence and Decision Support Systems have to everyone in the healthcare sector engaged to quality-of-care, i.e., making sure that doctors, nurses, and staff have the training and tools they need to do their jobs.

Keywords

Artificial intelligence Decision support systems Medical imaging Deep learning Logic programming Knowledge representation and reasoning Artificial neural networks Big data 

References

  1. 1.
    Institute for Health Technology Transformation (2013) Transforming health care through big data – strategies for leveraging big data in the health care industry. Institute for Health Technology Transformation EditionGoogle Scholar
  2. 2.
    Wang Y, Kung LA, Terry Anthony Byrd TA (2018) Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol Forecast Soc Change 126:3–13CrossRefGoogle Scholar
  3. 3.
    Archenaa J, Mary-Anita EA (2015) A survey of big data analytics in healthcare and government. Proc Comput Sci 50:408–413CrossRefGoogle Scholar
  4. 4.
    Wang Y, Kung LA, Wang WY, Cegielski CG (2018) An integrated big data analytics-enabled transformation model: application to health care. Information & Management 55:67–79Google Scholar
  5. 5.
    Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Pearson, Harlow, UKzbMATHGoogle Scholar
  6. 6.
    Holland J (1962) Outline for logical theory of adaptative systems. J ACM 9:297–314CrossRefzbMATHGoogle Scholar
  7. 7.
    Fogel L, Owens A, Walsh M (1966) Artificial intelligence through a simulated evolution. John Wiley & SonsGoogle Scholar
  8. 8.
    Koza J (1992) Genetic programming. MIT Press, Cambridge, USAzbMATHGoogle Scholar
  9. 9.
    Jong K (2006) Evolutionary computation a unified approach. MIT Institute for Health Technology Transformation Press, Cambridge, USAzbMATHGoogle Scholar
  10. 10.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRefGoogle Scholar
  11. 11.
    Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E (2015) Deep learning applications and challenges in big data analytics. J Big Data 2:1–21CrossRefGoogle Scholar
  12. 12.
    Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representation by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing: explorations in the microstructure of cognition, vol 1. MIT Press, Cambridge, pp 318–362Google Scholar
  13. 13.
    Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536CrossRefzbMATHGoogle Scholar
  14. 14.
    Nuti S, Vainieri M (2012) Managing waiting times in diagnostic medical imaging. BMJ Open 2:e001255CrossRefGoogle Scholar
  15. 15.
    McEnery KW (2013) Radiology information systems and electronic medical records. In IT Reference Guide for the Practicing Radiologist, pp. 1–14, American College of Radiology, USAGoogle Scholar
  16. 16.
    Fotiadou A (2013) Choosing and Visualizing Waiting Time Indicators in Diagnostic Medical Imaging Department for Different Purposes and Audiences. Master’s Thesis in Health Informatics, Karolinska Institutet, SwedenGoogle Scholar
  17. 17.
    Haykin S (2009) Neural networks and learning machines, 3rd edn. Pearson Education, New Jersey, USAGoogle Scholar
  18. 18.
    Fernandes F, Vicente H, Abelha A, Machado J, Novais P, Neves J (2015) Artificial neural networks in diabetes control. Proc 2015 Sci Info Conf (SAI 2015): 362–370, IEEE EdGoogle Scholar
  19. 19.
    Silva A, Vicente H, Abelha A, Santos MF, Machado J, Neves J, Neves J (2016) Length of stay in intensive care units – a case base evaluation. In: Fujita H, Papadopoulos GA (eds) New trends in software methodologies, tools and techniques, frontiers in artificial intelligence and applications, vol 286. IOS Press, Amsterdam, pp 191–202Google Scholar
  20. 20.
    Kakas A, Kowalski R, Toni F (1998) The role of abduction in logic programming. In: Gabbay D, Hogger C, Robinson I (eds) Handbook of logic in artificial intelligence and logic programming, vol 5. Oxford University Press, Oxford, pp 235–324Google Scholar
  21. 21.
    Neves J (1984) A logic interpreter to handle time and negation in logic databases. In: Muller R, Pottmyer J (eds) Proceedings of the 1984 annual conference of the ACM on the 5th generation challenge. Association for Computing Machinery, New York, pp 50–54Google Scholar
  22. 22.
    O’Neil P, O’Neil B, Chen X (2009) Star schema benchmark. Revision 3, June 5, http://www.cs.umb.edu/~poneil/StarSchemaB.pdf, last accessed 2018/01/23
  23. 23.
    Vicente H, Dias S, Fernandes A, Abelha A, Machado J, Neves J (2012) Prediction of the quality of public water supply using artificial neural networks. Journal of Water Supply: Research and Technology – AQUA 61:446–459CrossRefGoogle Scholar
  24. 24.
    Vicente H, Couto C, Machado J, Abelha A, Neves J (2012) Prediction of water quality parameters in a reservoir using artificial neural networks. Int J Design Nat Ecodynam 7:309–318CrossRefGoogle Scholar
  25. 25.
    Vicente H, Roseiro J, Arteiro J, Neves J, Caldeira AT (2013) Prediction of bioactive compound activity against wood contaminant fungi using artificial neural networks. Can J For Res 43:985–992CrossRefGoogle Scholar
  26. 26.
    Florkowski CM (2008) Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. Clin Biochem Rev 29(Suppl 1):S83–S87Google Scholar
  27. 27.
    Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks: a tutorial. Computer 29:31–44CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • José Neves
    • 1
  • Henrique Vicente
    • 1
    • 2
  • Marisa Esteves
    • 3
  • Filipa Ferraz
    • 3
  • António Abelha
    • 1
  • José Machado
    • 1
  • Joana Machado
    • 4
  • João Neves
    • 5
  • Jorge Ribeiro
    • 6
  • Lúzia Sampaio
    • 7
  1. 1.Centro AlgoritmiUniversidade do MinhoBragaPortugal
  2. 2.Departamento de Química, Escola de Ciências e Tecnologia, Centro de Química de ÉvoraUniversidade de ÉvoraÉvoraPortugal
  3. 3.Departamento de InformáticaUniversidade do MinhoBragaPortugal
  4. 4.Farmácia de LamaçãesBragaPortugal
  5. 5.Mediclinic Arabian RanchesDubaiUnited Arab Emirates
  6. 6.Escola Superior de Tecnologia e Gestão, ARC4DigiT – Applied Research Center for Digital TransformationInstituto Politécnico de Viana do CasteloViana do CasteloPortugal
  7. 7.Dubai Healthcare CityDubaiUAE

Personalised recommendations