Advertisement

Prediction of Pediatric Risk Using a Hybrid Model Based on Soft Computing Techniques

  • Yanet Rodríguez
  • Mabel González
  • Adonis Aguirre
  • Mayelis Espinosa
  • Ricardo Grau
  • Joaquín O. García
  • Luis E. Rovira
  • Maria M. García
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5317)

Abstract

We present an automatic system for the prediction of mortality risk in pediatric patients, which uses Soft Computing techniques instead of traditional ones based on score. The hybrid model applied combines both Case-Based Reasoning and Artificial Neural Networks with fuzzy set theory, taking its applications the advantages of these approaches. While the new way of prediction, named SAPRIM (Automated Predictor System of Infant Mortality Risk), was automatically defined from domain examples reducing the knowledge engineering effort, the experimental results using cross validation showed good accuracy with respect to other traditional classifiers. Besides, SAPRIM allows a more natural framework to include expert knowledge by using linguistic terms. After this automatic system was exploited by human experts for a year, the field evaluation corroborates good results.

Keywords

Artificial Neural Network Hybrid Model Pediatric Intensive Care Unit Linguistic Term Human Expert 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Riesbeck, C., Schank, R.: Inside Case-Based reasoning. Lawrence Erlbaum Associates, New Jersey (1989)Google Scholar
  2. 2.
    Kolodner, J.: An introduction to case-based reasoning. Artificial Intelligence Review 6, 3–34 (1992)CrossRefGoogle Scholar
  3. 3.
    Bergmann, R., Breen, S., Göker, M., Manago, M., Wess, S.: Developing IndustrialCBR Applications. In: Bergmann, R., Althoff, K.-D., Breen, S., Göker, M.H., Manago, M., Traphöner, R., Wess, S. (eds.) Developing Industrial Case-Based Reasoning Applications. LNCS (LNAI), vol. 1612. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Zadeh, L.A.: From Computing with Numbers to Computing with Words -From Manipulation of Measurements to Manipulation of Perceptions. In: Intelligent Systems and Soft Computing, pp. 3–40 (2000)Google Scholar
  5. 5.
    Pal, S.K., Shiu, S.C.K.: Foundations of soft case-based reasoning. Wiley series on intelligent systems. John Wiley & Sons Inc., Hoboken (2004)CrossRefGoogle Scholar
  6. 6.
    Weber, R.O.: Fuzzy Set Theory and Uncertainty in Case-Based Reasoning. Engineering Intelligent Systems (2006)Google Scholar
  7. 7.
    Hüllermeier, E.: Case-Based Approximate Reasoning. Series B: Mathematical and Statistical Methods, vol. 44. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  8. 8.
    Zadeh, L.A.: Soft Computing and Fuzzy Logic. IEEE Software 11(6), 48–56 (1994)CrossRefGoogle Scholar
  9. 9.
    Kuncheva, L., Steimann, F.: Fuzzy diagnosis. Artificial Intelligence in Medicine 16(2), 121–128 (1999)CrossRefGoogle Scholar
  10. 10.
    Buisson, J.-C.: Approximate reasoning in computer-aided medical decision systems. In: Practical Applications of Fuzzy Technologies. Springer, Heidelberg (1999)Google Scholar
  11. 11.
    Pollack, M., Ruttimann, V., Getson, P.: Pediatric Risk of Mortality (PRIMS) score. Crit Care Med., 1011 (1988)Google Scholar
  12. 12.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Rodriguez, Y., Garcia, M.M., De Baets, B., Bello, R., Morell, C.: Extending a Hybrid CBR-ANN Model by Modeling Predictive Attributes using Fuzzy Sets. In: Dumke, R.R., Abran, A. (eds.) IWSM 2000. LNCS, vol. 2006, pp. 238–248. Springer, Heidelberg (2001)Google Scholar
  14. 14.
    Rodriguez, Y., Garcia, M.M., Baets, B.D., Morell, C., Bello, R.: A Connectionist Fuzzy Case-Based Reasoning Model. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 176–185. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Rodriguez, Y., García, M.M., De Baets, B., Morell, C., Bello, R.: Integrating ANN and CBR using Fuzzy sets to develop Hybrid Systems. In: Hybrid Artificial Intelligence Systems. HAIS 2006 (2007)Google Scholar
  16. 16.
    García, M.M., Bello, R.: A model and its different applications to case-based reasoning, vol. 9(7), pp. 465–473 (1996)Google Scholar
  17. 17.
    Lacroix, J., Cotting, J.: Severity of illness and organ dysfunction scoring in children. Pediatr Crit Care Med., 126–134 (2005)Google Scholar
  18. 18.
    Mitchell, T.: The Need for Biases in Learning Generalizations. Readings in Machine Learning (1980)Google Scholar
  19. 19.
    Ruspini, E.: A new approach to clustering. Information and Control 15, 22–38 (1969)CrossRefzbMATHGoogle Scholar
  20. 20.
    le Cessie, S., van Houwelingen, J.C.: Ridge Estimators in Logistic Regression. Applied Statistics 41(1), 191–201 (1992)CrossRefzbMATHGoogle Scholar
  21. 21.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufman, San Mateo (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yanet Rodríguez
    • 1
  • Mabel González
    • 1
  • Adonis Aguirre
    • 2
  • Mayelis Espinosa
    • 1
  • Ricardo Grau
    • 1
  • Joaquín O. García
    • 2
  • Luis E. Rovira
    • 2
  • Maria M. García
    • 1
  1. 1.Centro de Estudios de InformáticaUniversidad Central de Las VillasCuba
  2. 2.Hospital Pediátrico Universitario “José Luis Miranda” de Santa ClaraVilla ClaraCuba

Personalised recommendations