Predicting Outcomes of Septic Shock Patients Using Feature Selection Based on Soft Computing Techniques

1. American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Crit. Care Med. (20), 864–874 (1992)

   Google Scholar 

2. Burchardi, H., Schneider, H.: Economic aspects of severe sepsis: a review of intensive care unit costs, cost of illness and cost effectiveness of therapy. Pharmacoeconomics 22(12), 793–813 (2004)

   Article  Google Scholar 

3. Paetza, J., Arlt, B., Erz, K., Holzer, K., Brause, R., Hanisch, E.: Data quality aspects of a database for abdominal septic shock patients. Computer Methods and Programs in Biomedicine 75, 23–30 (2004)

   Article  Google Scholar 

4. Paetza, J.: Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions. Artificial Intelligence in Medicine 28, 207–230 (2003)

   Article  Google Scholar 

5. Mendonça, L.F., Vieira, S.M., Sousa, J.M.C.: Decision tree search methods in fuzzy modeling and classification. International Journal of Approximate Reasoning 44(2), 106–123 (2007)

   Article  MathSciNet  Google Scholar 

6. Kuncheva, L.I.: Fuzzy Classifier Design. Springer, Heidelberg (2000)

   MATH  Google Scholar 

7. van den Berg, J., Kaymak, U., van den Bergh, W.M.: Fuzzy classification using probability-based rule weighting. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2002, vol. 2, pp. 991–996 (2002)

   Google Scholar 

8. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man and Cybernetics 15(1), 116–132 (1985)

   MATH  Google Scholar 

9. Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems 1(1), 7–31 (1993)

   Article  Google Scholar 

10. Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice-Hall, Upper Saddle River (2008)

    Google Scholar 

11. Jensen, R., Shen, Q.: Are more features better? a response to attributes reduction using fuzzy rough sets. IEEE Transactions on Fuzzy Systems 17(6), 1456–1458 (2009)

    Article  Google Scholar 

12. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)

    Article  MATH  Google Scholar 

13. Vieira, S.M., Mendonça, L., Sousa, J.M.C.: Modified regularity criterion in dynamic fuzzy modeling applied to industrial processes. In: Proc. of 2005 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2005, Reno, Nevada, May 2005, pp. 483–488 (2005)

    Google Scholar 

14. Pekalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence). World Scientific Publishing Co., Inc., River Edge (2005)

    Book  MATH  Google Scholar 

15. Dorigo, M., Birattari, M., Stützle, T.: Ant colony optimization. IEEE Computational Intelligence Magazine 1(4), 28–39 (2006)

    Google Scholar 

16. Vieira, S.M., Sousa, J.M.C., Runkler, T.A.: Two cooperative ant colonies for feature selection using fuzzy models. Expert Systems with Applications 37(4), 2714–2723 (2010)

    Article  Google Scholar 

17. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley–Interscience Publication, Chichester (2001)

    MATH  Google Scholar 

18. Hanisch, E., Brause, R., Arlt, B., Paetz, J., Holzer, K.: The MEDAN Database (2003), http://www.medan.de (accessed October 20, 2009)

Download references