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Optimization Techniques to Detect Early Ventilation Extubation in Intensive Care Units

  • Pedro Oliveira
  • Filipe PortelaEmail author
  • Manuel F. Santos
  • José Machado
  • António Abelha
  • Álvaro Silva
  • Fernando Rua
Conference paper
  • 1.2k Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 445)

Abstract

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hybrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2, 93.1, 92.97 % respectively, thus showing their feasibility to work in a real environment.

Keywords

Optimization techniques Decision support systems Machine learning Heuristics Intensive care units extubation 

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References

  1. 1.
    Santos, M.F., Portela, F., Vilas-Boas, M.: INTCARE -Multi-agent Approach for Real-time Intelligent Decision Support in Intensive Medicine. In: ICAART 2011 - International Conference on Agents and Artificial Intelligence. pp 364-369. SciTePress. (2011)Google Scholar
  2. 2.
    Portela, F., Pinto, Santos, M. F.: Data Mining Predictive Models For Pervasive Intelligent Decision Support In Intensive Care Medicine. In: KMIS 2012 - Knowledge Management and Information Sharing. pp 81-88. SciTePress (2012)Google Scholar
  3. 3.
    Oliveira, S., Portela, F., Santos, M. F., Neves, J., Silva, Á. Rua, F.: Feature selection for detecting patients with weaning failures in Intensive Medicine. In: Mathematics and Computers in Sciences and Industry. Volume 50, pp 195-200. CPS (2015)Google Scholar
  4. 4.
    Ramon, J., Fierens, D., Güiza, F., Meyfroidt, G., Blockeel, H., Bruynooghe, M., & Van Den Berghe, G.: Mining data from intensive care patients. In: Advanced Engineering Informatics, 21(3), 243–256. doi: 10.1016/j.aei.2006.12.002. (2007)Google Scholar
  5. 5.
    De Turck, F., Decruyenaere, J., Thysebaert, P., Van Hoecke, S., Volckaert, B., Danneels, C., De Moor, G.: Design of a flexible platform for execution of medical decision support agents in the intensive care unit. In: Computers in Biology and Medicine, 37, 97–112. 2007)Google Scholar
  6. 6.
    Kaynar, A. and Sharma, S.: Respiratory Failure. 39. Available: http://emedicine.medscape.com/article/167981-print. Accessed Dec, 2015
  7. 7.
    Tehrani, F. T.: Automatic control of mechanical ventilation. Part 2: the existing techniques and future trends. In: Journal of clinical monitoring and computing, vol. 22, pp. 417-424. (2008)Google Scholar
  8. 8.
    Stawicki, S. P.: Mechanical ventilation: weaning and extubation (2007)Google Scholar
  9. 9.
    Alves, C. J. S., Pardalos, M. P., Vicente, L. N.: In: Optimization in Medicine, Springer Optimization and its Applications Series, Vol. 12. Springer (2008)Google Scholar
  10. 10.
    Gilli, M., & Winker, P.: A review of heuristic optimization methods in econometrics. In: Heuristic Optimization Methods in Econometrics (2008)Google Scholar
  11. 11.
    Birattari, M., Paquete, L., Stützle, T., Varrentrapp, K.: Classification of Metaheuristics and Design of Experiments for the Analysis of Components, In: Technical Report AIDA-01-05, FG Intellektik, FB Informatik, Technische Universität Darmstadt, Darmstadt, Germany. (2001)Google Scholar
  12. 12.
    Boussaïd, I., Lepagnot, J., & Siarry, P.: A survey on optimization metaheuristics. In: Information Sciences, 237, 82–117 (2013)Google Scholar
  13. 13.
    Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press, Oxford, UK. (1996)Google Scholar
  14. 14.
    Oliveira, P., Portela, C.F., Santos, M.F., Silva, Á., Machado, J., Abelha, A.: Machine Learning: an overview of optimization techniques. In: Recente Advances in Computer Science, Series 32, 2015, pp 51-56. INASE (2015)Google Scholar
  15. 15.
    Alcalá-Fdez, J., Sánchez, L., Garcia, S., del Jesus, M.J., Ventura, S. Garrel, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V.M., Fernández, J.C., Herrera, F.: KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems. Soft Computing 13:3 (2009)Google Scholar
  16. 16.
    Dietterich, T.G.: Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. In: Neural Computation 10:7 (1998)Google Scholar
  17. 17.
    Carvalho, D.R., Freitas, A., A.: A hybrid decision tree/genetic algorithm method for data mining. In: Information Sciences 163:1, 13-35 (2004)Google Scholar
  18. 18.
    Bernadó-Mansilla, E., Garrel, J., M.: Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks. In: Evolutionary Computation 11:3, 209-238 (2003)Google Scholar
  19. 19.
    Wang, J., Neskovic, P., Cooper, L., N.: Improving nearest neighbor rule with a simple adaptative distance measure. In: Pattern Recognition Letters 28, 207-213 (2007)Google Scholar
  20. 20.
    Sheskin, D.: Handbook of parametric and nonparametric statistical procedures. Chapman and Hall/CRC (2003)Google Scholar
  21. 21.
    Doksum, K.: Robust procedures for some linear models with one observation per cell. In: Annals of Mathematical Statistics 38, 878-883 (1967)Google Scholar
  22. 22.
    Portela, F., Santos, M., Machado, J., Silva, A., Abelha. A. Pervasive and Intelligent Decision Support in Critical Health Care using Ensemble. In: Lecture Notes in Computer Science (LNCS) - Information Technology in Bio- and Medical Informatics. Volume 8060, 2013, pp 1-16. ISBN: 978-3-642-40093-3. Springer (2013)Google Scholar
  23. 23.
    Portela, F., Santos, M., Machado, J., Silva, A., Abelha. A.: Pervasive Ensemble Data Mining Models to Predict Organ Failure and Patient Outcome in Intensive Medicine. In: Communications in Computer and Information Science. Volume 415, 2013, pp 410-425. ISBN: 978-3-642-54104-9. Springer (2013)Google Scholar
  24. 24.
    Portela, F., Santos, M., Vilas-Boas, M., Rua, F., Silva, Á., Neves, J.: Real-time Intelligent decision support in intensive medicine. In: KMIS 2010-International Conference on Knowledge Management and Information Sharing, Valência, Espanha, p. 7 (2010)Google Scholar
  25. 25.
    Oliveira, S., Portela, F., Santos, M., Machado, J., Silva, A., Abelha. A, Rua, F.: Predicting Plateau Pressure in Intensive Medicine for Ventilated patients. In: Advances in Intelligent Systems and Computing (WorldCist 2015 - Healthcare Information Systems: Interoperability, Security and Efficiency Workshop). Volume 354, 2015, pp 179-188. ISBN: 978-3-319-16527-1. Springer (2015)Google Scholar
  26. 26.
    Portela, C.F., Santos, M.F., Silva, Á., Machado, J., Abelha, A.: Enabling a Pervasive Approach for Intelligent Decision Support in Critical Health Care. In: Cruz-Cunha, M.M., Varajão, J., Powell, P., Martinho, R. (eds.) CENTERIS 2011, Part III. CCIS, vol. 221, pp. 233–243. Springer, Heidelberg (2011)Google Scholar
  27. 27.
    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)Google Scholar
  28. 28.
    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: Lecture Notes in Computer Science (LNCS) - Information Technology in Bio- and Medical Informatics. Volume 8649, 2014, pp 87-102. Springer (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Pedro Oliveira
    • 1
  • Filipe Portela
    • 1
    • 2
    Email author
  • Manuel F. Santos
    • 1
  • José Machado
    • 1
  • António Abelha
    • 1
  • Álvaro Silva
    • 3
  • Fernando Rua
    • 3
  1. 1.Algoritmi Research CentreUniversity of MinhoGuimarãesPortugal
  2. 2.ESEIGPorto PolytechnicPortoPortugal
  3. 3.Intensive Care UnitCentro Hospitalar do PortoPortoPortugal

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