Skip to main content

Modified Sequential Forward Selection Applied to Predicting Septic Shock Outcome in the Intensive Care Unit

  • Conference paper

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 190)

Abstract

Medical databases often contain large amounts of missing data. This poses very strict constraints to the use of exclusively computer-based feature selection techniques. Moreover, in medical data there is usually no unique combination of features that provides the best explanation of the outcome. In this paper we propose a modified Sequential Forward Selection (SFS) approach to the problem of selecting sets of physiologic variables from septic shock patients in order to predict their outcome. We were able to achieve ten different combinations of only three physiological numerical parameters, all performing better than the best set suggested up to now. The performances of these sets are higher than 0.97 for AUC and up to 0.97 for accuracy.

Keywords

  • Fuzzy systems
  • medical data
  • modified sequential forward selection
  • septic shock

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-33042-1_50
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   269.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-33042-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   349.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Berthold, M.R.: Fuzzy-models and potential outliers. In: Proc. of the 18th Int. Conf. of the North America Fuzzy Information Processing Society. IEEE Press (1999)

    Google Scholar 

  2. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10, 191–203 (1984)

    CrossRef  Google Scholar 

  3. Brause, R., Hamker, F., Paetz, J.: Septic Shock Diagnosis by Neural Networks and Rule Based Systems. In: Computational Intelligence Techniques in Medical Diagnosis and Prognosis. Springer US (2001)

    Google Scholar 

  4. 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, 793–813 (2004)

    CrossRef  Google Scholar 

  5. Dellinger, R.P., et al.: Surviving Sepsis Campaign: International guidelines for management of severe sepsis and septic shock: 2008. Crit. Care Med. 36, 1394–1396 (2008)

    Google Scholar 

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

  7. Hua, J., Xiong, Z., Lowey, J., Suh, E., Dougherty, E.R.: Optimal number of features as a function of sample size for various classification rules. Bioinform. 21, 1509–1515 (2005)

    CrossRef  Google Scholar 

  8. MathWorks: MatLab R2012a Documentation for Fuzzy Logic Toolbox (2012), http://www.mathworks.com

  9. Paetz, J.: Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions. Artif. Intel. Med. 28, 207–230 (2003)

    CrossRef  Google Scholar 

  10. Paetz, J., Arlt, B.: A Neuro-fuzzy Based Alarm System for Septic Shock Patients with a Comparison to Medical Scores. In: Colosimo, A., Giuliani, A., Sirabella, P. (eds.) ISMDA 2002. LNCS, vol. 2526, pp. 42–52. Springer, Heidelberg (2002)

    CrossRef  Google Scholar 

  11. Paetz, J., Arlt, B., Erz, K., Holzer, K., Brause, R., Hanisch, E.: Data quality aspects of a database for abdominal septic shock patients. Comput. Meth. Prog. Biomed. 75, 23–30 (2004)

    CrossRef  Google Scholar 

  12. Pal, N.R., Bezdek, J.C.: On cluster validity for fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3, 370–379 (1995)

    CrossRef  Google Scholar 

  13. Pereira, R.D., et al.: Predicting Septic Shock Outcomes in a Database with Missing Data using Fuzzy Modeling. In: Proc. of the IEEE Int. Conf. on Fuzzy Systems (2011)

    Google Scholar 

  14. Takagi, T., Sugeno, M.: Fuzzy identification of system and its applications to modelling and control. IEEE Trans. Syst. Man Cyb. 15, 116–132 (1985)

    MATH  CrossRef  Google Scholar 

  15. Xie, X.L., Beni, G.: A Validity Measure for Fuzzy Clustering. IEEE Trans. Pattern Anal. Mach. Intel. 13, 841–847 (1991)

    CrossRef  Google Scholar 

  16. Yan, S.Y., Sun, Z.Q.: Universal Approximation for Takagi-Sugeno Fuzzy Systems Combining Statically and Dynamically Constructive Methods-MISO Cases. In: Proc. of the Int. Conf. on Information Management and Engineering (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rúben Duarte Pereira .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pereira, R.D., Sousa, J., Vieira, S., Reti, S., Finkelstein, S. (2013). Modified Sequential Forward Selection Applied to Predicting Septic Shock Outcome in the Intensive Care Unit. In: Kruse, R., Berthold, M., Moewes, C., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33042-1_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33041-4

  • Online ISBN: 978-3-642-33042-1

  • eBook Packages: EngineeringEngineering (R0)