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A Real-Time Intelligent System for Tracking Patient Condition

  • Filipe PortelaEmail author
  • Sérgio Oliveira
  • Manuel Santos
  • José Machado
  • António Abelha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9456)

Abstract

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance.

Keywords

Ambient intelligence Healthcare Data streaming Critic events Intelligent systems Real-time Tracking system Intcare Intensive care 

Notes

Acknowledgments

This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project UID/CEC/00319/2013 and PTDC/EEI-SII/1302/2012.

References

  1. 1.
    Santos, M., Azevedo, C.: Data Mining Descoberta do conhecimento em base de dados. FCA - Editora de Informática, Lda (2005)Google Scholar
  2. 2.
    Portela, F., Santos, M.F., Gago, P., Silva, Á., Rua, F., Abelha, A.,. Neves, J.: Enabling real-time intelligent decision support in intensive care. In: ESM 2011 (2011)Google Scholar
  3. 3.
    Marins, F.A.S., Rodrigues, R., Portela, F., Santos, M.F., Abelha, A., Machado, J.M.: Extending a patient monitoring system with identification and localisation. In: 2013 IEEM (2013)Google Scholar
  4. 4.
    Koh, H., Tan, G.: Data mining applications in healthcare. J. Healthc. Inf. Manage. 19(2), 64–72 (2005)Google Scholar
  5. 5.
    Acampora, G., Cook, D.J., Rashidi, P., Vasilakos, A.V.: A survey on ambient intelligence in healthcare. Proc. IEEE 101(12), 2470–2494 (2013)CrossRefGoogle Scholar
  6. 6.
    Augusto, J.C.: Ambient intelligence: basic concepts and applications. In: Filipe, J., Shishkov, B., Helfert, M. (eds.) Software and Data Technologies, pp. 16–26. Springer, Berlinl, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Machado, J.M., Abelha, A., Neves, J., Santos, M.: Ambient intelligence in medicine. In: IEEE BioCAS 2006, Art. No. 4600316, pp. 1–4 (2006)Google Scholar
  8. 8.
    Bricon-Souf, N., Newman, C.R.: Context awareness in health care: a review. Int. J. Med. Inform. 76(1), 2–12 (2007)CrossRefGoogle Scholar
  9. 9.
    Rothschild, J.M., Landrigan, C.P., Cronin, J.W., Kaushal, R., Lockley, S.W., Burdick, E., et al.: The critic care safety study: the incidence and nature of adverse events and serious medical errors in intensive care. Crit. Care Med. 33(8), 1694–1700 (2005)CrossRefGoogle Scholar
  10. 10.
    Portela, F., Gago, P., Santos, M.F., Machado, J.M., Abelha, A., Silva, Á., Rua, F.: Implementing a pervasive real-time intelligent system for tracking critic events in intensive care patients. In: IJHISI (2013)Google Scholar
  11. 11.
    Kaur, M., Pawar, M., Kohli, J.K., Mishra, S.: Critic events in intensive care unit. Indian J. Crit. Care Med. 12(1), 28–31 (2008)CrossRefGoogle Scholar
  12. 12.
    Keegan, M.T., Gajic, O., Afessa, B.: Severity of illness scoring systems in the intensive care unit. Crit. Care Med. 39(1), 163–169 (2011)CrossRefGoogle Scholar
  13. 13.
    Silva, A., Cortez, P., Santos, M.F., Gomes, L., Neves, J.: Rating organ failure via adverse events using data mining in the intensive care unit. Artif. Intell. Med. 43(3), 179–193 (2008)CrossRefGoogle Scholar
  14. 14.
    Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F.: Pervasive and intelligent decision support in intensive medicine – the complete picture. In: Bursa, M., Khuri, S., Renda, M.E. (eds.) ITBAM 2014. LNCS, vol. 8649, pp. 87–102. Springer, Heidelberg (2014)Google Scholar
  15. 15.
    Portela, F., Santos, M.F., Vilas-Boas, M.: A pervasive approach to a real-time intelligent decision support system in intensive medicine. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds.) IC3 K 2010. CCIS, vol. 272, pp. 368–381. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  16. 16.
    Portela, F., Gago, P., Santos, M.F., Silva, A., Rua, F., Machado, J., Abelha, A., Neves, J.: Knowledge discovery for pervasive and real-time intelligent decision support in intensive care medicine. In: KMIS 2011, p. 12. Paris, France (2011)Google Scholar
  17. 17.
    Santos, M.F., Portela, F., Vilas-Boas, M., Machado, J., Abelha, A., Neves, J.: Information architecture for intelligent decision support in intensive medicine. Trans. Comput. 8(5), 810–819 (2009). (World Scientific and Engi)Google Scholar
  18. 18.
    Portela, F., Aguiar, J., Santos, M.F., Silva, Á., Rua, F.: Pervasive Intelligent Decision Support System - Technology Acceptance in Intensive Care Units. In: AISC, Springer, Berlin (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Filipe Portela
    • 1
    • 2
    Email author
  • Sérgio Oliveira
    • 1
  • Manuel Santos
    • 1
  • José Machado
    • 1
  • António Abelha
    • 1
  1. 1.Algoritmi Research CentreUniversity of MinhoBragaPortugal
  2. 2.ESEIGPorto PolytechnicPortoPortugal

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