Skip to main content
Springer Nature Link
Log in

Towards a Medical Intensive Care Unit Decision Support System Based on Intuitionistic Fuzzy Logic

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

  • 1708 Accesses

Abstract

Intensive Care Unit (ICU) medical processes can be so complex and unpredictable that physicians sometimes must make decisions based on perception. Both decision support system and Intuitionistic Fuzzy Logic (IFL) techniques can assist doctors to handle this complexity in a safe, harmless and efficient manner. To this end, we propose a prototype called Medical Intuitionistic Fuzzy Expert Decision Support System (MIFEDSS) based on IFL and the Modified Early Warning Score (MEWS) standard score. Moreover, the experimental results have been shown the efficiency of the proposed system.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (France)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 245.03
Price includes VAT (France)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 316.49
Price includes VAT (France)
  • Compact, lightweight 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

Institutional subscriptions

Similar content being viewed by others

References

  1. Mahfouf, M., Abbod, M., Linkens, F.: A survey of fuzzy logic monitoring and control utilization in medicine. J. Artif. Intell. Med. 21, 27–42 (2001)

    Article  Google Scholar 

  2. Semotok, C., Andrysek, J., Basir, O., Otto, E.: An intelligent diabetes software prototype: predicting blood glucose levels and recommending regimen changes. Diab. Technol. Ther. 2(4), 569–576 (2000)

    Article  Google Scholar 

  3. Shereck, D., Jabur, F.: Implementation of a fuzzy logic based expert system to control insulin-pump doses. Mcgill University, ECE Department, Computer Architecture Laboratory (2005)

    Google Scholar 

  4. Yue, G., Yi, G.: Application study in decision support with fuzzy cognitive map. Int. J. Comput., 324–331(2007)

    Google Scholar 

  5. Bartolomeo, C.: Off-line control of the postprandial glycaemia in type one diabetes patients by a fuzzy logic decision support. Expert Syst. With Appl. 39(12), 10693–10699 (2012)

    Article  Google Scholar 

  6. Adeli, A., Nashat, M.: A fuzzy expert system for heart disease diagnosis. In: International Multi Conference of Engineers and Computer Scientists, vol. 1 (2010)

    Google Scholar 

  7. Kar, S., Majumder, D.: An investigative study on early diagnosis of breast cancer using a new approach of mathematical shape theory and neuro-fuzzy classification system. Int. J. Fuzzy Syst. 18(3), 1–18 (2015)

    MathSciNet  Google Scholar 

  8. Qu, Y., Shang, C., Shen, Q., Parthaláin, N.M., Wu, W.: Kernel-based fuzzy-rough nearestneighbour classification for mammographic risk analysis. Int. J. Fuzzy Syst. 17(3), 471–483 (2015)

    Article  MathSciNet  Google Scholar 

  9. Jayachandran, A., Sundararaj, G.K.: Abnormality segmentation and classification of multi-class brain tumor in MR images using fuzzy logic-based hybrid kernel SVM. Int. J. Fuzzy Syst. 17(3), 434–443 (2015)

    Article  MathSciNet  Google Scholar 

  10. Bingzhen, S., Weimin, M., Chen, X.: Fuzzy rough set on probabilistic approximation space over two universes and its application to emergency decision-making. Expert Syst. 32(4), 507–521 (2015)

    Article  Google Scholar 

  11. Atanassov, K.: Intuitionistic fuzzy sets, fuzzy sets and systems. Fuzzy Sets Syst. 20, 87–96 (1986)

    Article  MATH  Google Scholar 

  12. Jeong, Y., Kyung, S., Sun, Y., Chong, D.: An application of interval valued intuitionistic fuzzy sets for medical diagnosis of headache. Int. J. Innovative Comput. Inf. Control ICIC 7(5(B)), 2755–2762 (2011)

    Google Scholar 

  13. Eulalia, S., Janusz, K.: Intuitionistic fuzzy sets in some medical applications. In: Fifth International Conference on IFSs. NIFS 7, pp. 58–64 (2001)

    Google Scholar 

  14. Pathinathan, T., Jon, A., Ilavarasi, P.: An application of interval valued intuitionistic fuzzy sets in medical diagnosis using logical operators. Int. J. Comput. Algorithm 3, 495–498 (2014)

    Google Scholar 

  15. Mohammed, M.: Medical diagnosis via interval valued intuitionistic fuzzy sets. Ann. Fuzzy Math. Inf. (2012). ISSN 2093–9310

    Google Scholar 

  16. Chetia, B., Das, P.K.: An application of interval-valued fuzzy soft sets in medical diagnosis. Int. J. Contemp. Math. Sci. 5(38), 1887–1894 (2010)

    MATH  Google Scholar 

  17. Hoda, D., Mohammadreza, A.: A novel application of intuitionistic fuzzy sets theory in medical science: Bacillus colonies recognition. Artif. Intell. Res. 2(2), 1–17 (2013)

    Google Scholar 

  18. Boquan, L., Zhang, H., Li, Y.: The molds of intuitionistic fuzzy value and their applications. Int. J. Fuzzy Syst. 18(2), 1–15 (2015)

    MathSciNet  Google Scholar 

  19. Vahid, K.: Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition. Artif. Intell. Med. 47, 43–52 (2009)

    Article  Google Scholar 

  20. Jemal, H., Kechaou, Z., Ayed, M.B., Alimi, A.M.: A multi agent system for hospital organization. Int. J. Mach. Learn. Comput. 5(1), 51–56 (2015)

    Article  Google Scholar 

  21. Jemal, H., Kechaou, Z., Ayed, M.B.: Swarm intelligence and multi agent system in healthcare. In: 6th International Conference of Soft Computing and Pattern Recognition, pp. 423–427. IEEE (2014)

    Google Scholar 

  22. Zadeh, L.: Fuzzy sets. Inf. Control. Inf. 8(3), 338–353 (1965)

    Article  Google Scholar 

  23. Hamid M., Dan, I., Jérôme, B., Jean, L., Lamine, B., Mohamed B., Bernadette D.: A fuzzy logic approach for remote healthcare monitoring by learning and recognizing human activities of daily living. In: Fuzzy Logic – Emerging Technologies and Applications, pp. 19–40 (2012)

    Google Scholar 

  24. Atanassov, K.: Intuitionistic Fuzzy Sets: Theory and Applications. Physica-Verlag, Heidelberg (1999)

    Book  MATH  Google Scholar 

  25. Shaddel, F., Khosla, V., Banerjee, S.: Effects of introducing MEWS on nursing staff in mental health in patient settings. Prog. Neurol. Psychiatry 18(2), 24–27 (2014)

    Article  Google Scholar 

  26. JFuzzyLogic Plugins. http://jfuzzylogic.sourceforge.net/html/manual.html

  27. Jemal, H., Kechaou, Z., Ayed, M.B.: An enhanced healthcare system in mobile cloud computing environment. Vietnam J. Comput. Sci. 3(4), 267–277 (2016)

    Article  Google Scholar 

  28. Jemal, H., Kechaou, Z., Ayed, M.B., Alimi, A.M.: Cloud computing and mobile devices based system for healthcare application. In: IEEE International Symposium on Technology and Society (2015). ISBN: 978-1-4799-8283-7

    Google Scholar 

  29. Jemal, H., Kechaou, Z., Ayed, M.B., Alimi, A.M.: Mobile cloud computing in healthcare system. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds.) Computational Collective Intelligence. LNCS, vol. 9330, pp. 408–417. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National School of Engineers (ENIS), REGIM: REsearch Groups on Intelligent Machines, BP 1173, 3038, Sfax, Tunisia

    Hanen Jemal, Zied Kechaou & Mounir Ben Ayed

  2. Computer Sciences Department, Higher School of Commerce (HSC), University of Sfax, Sfax, Tunisia

    Zied Kechaou

  3. Computer Sciences and Communication Department, Faculty of Sciences of Sfax (FSS), University of Sfax, Sfax, Tunisia

    Mounir Ben Ayed

Authors
  1. Hanen Jemal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zied Kechaou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mounir Ben Ayed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hanen Jemal .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Jemal, H., Kechaou, Z., Ben Ayed, M. (2017). Towards a Medical Intensive Care Unit Decision Support System Based on Intuitionistic Fuzzy Logic. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-53480-0_59

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation