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Synergies of Soft Computing and Statistics for Intelligent Data Analysis pp 469–477Cite as

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

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Part of the book series: Advances in Intelligent Systems and Computing ((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.

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Author information

Authors and Affiliations

  1. Instituto Superior Técnico, CIS/IDMEC-LAETA, Technical University of Lisbon, 1049-001, Lisbon, Portugal

    Rúben Duarte Pereira, João Sousa & Susana Vieira

  2. Massachusetts Institute of Technology, 02139, Cambridge, MA, USA

    Rúben Duarte Pereira & Stan Finkelstein

  3. Beth Israel Deaconess Medical Center, 02446, Boston, MA, USA

    Shane Reti

  4. Harvard Medical School, 02115, Boston, MA, USA

    Shane Reti & Stan Finkelstein

Authors
  1. Rúben Duarte Pereira
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  2. João Sousa
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  3. Susana Vieira
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  4. Shane Reti
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  5. Stan Finkelstein
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Corresponding author

Correspondence to Rúben Duarte Pereira .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Otto-von-Guericke University of Magdebur, Geb. 29, Raum 008, Universitätsplatz 2, Magdeburg, 39106, Germany

    Rudolf Kruse

  2. , FB Informatik & Informationswissenschaft, University of Konstanz, Konstanz, 78457, Germany

    Michael R. Berthold

  3. of Magdeburg, Faculty of Computer Science, Otto-von-Guericke University, Geb. 29, Universitätsplatz 2 008, Magdeburg, 39106, Germany

    Christian Moewes

  4. , Department of Statistics and OR, University of Oviedo, C/ Calvo Sotelo, s/n, Oviedo, 33007, Spain

    María Ángeles Gil

  5. Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warsaw, 01-447, Poland

    Przemysław Grzegorzewski

  6. Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warsaw, 01-447, Poland

    Olgierd Hryniewicz

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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

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  • 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)

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