Predicting Sepsis: A Comparison of Analytical Approaches

  • Femida Gwadry-Sridhar
  • Ali Hamou
  • Benoit Lewden
  • Claudio Martin
  • Michael Bauer
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 69)


Sepsis is a significant cause of mortality and morbidity and is often associated with increased hospital resource utilization, prolonged intensive care unit and hospital stay. With advances in medicine, there is now aggressive goal oriented treatments that can be used to help patients that may be at risk for sepsis. To predict this risk, we hypothesized that commonly used univariate and multivariate models could be enhanced by using multiple analytic methods to providing greater precision. As a first step, we analyze data about patients with and without sepsis using multiple regression, decision trees and cluster analysis. We compare the predictive accuracy of the three different approaches in predicting which patients are likely (or not likely) to develop sepsis. The precision analysis suggests that decision trees may provide a better predictive model than either regression methods or cluster analysis.


Intensive Care Unit Decision Tree Septic Shock Severe Sepsis Intensive Care Unit Patient 
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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2011

Authors and Affiliations

  • Femida Gwadry-Sridhar
    • 1
  • Ali Hamou
    • 1
  • Benoit Lewden
    • 1
  • Claudio Martin
    • 2
  • Michael Bauer
    • 3
  1. 1.I-THINK Research LabLawson Health Research InstituteLondonCanada
  2. 2.Dept of Medicine and PhysiologyUniversity of Western OntarioLondonCanada
  3. 3.Dept of Computer ScienceUniversity of Western OntarioLondonCanada

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