Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study.

Journal: The Lancet. Respiratory medicine
Published Date: Nov 24, 2014

Abstract

BACKGROUND: Improved mortality prediction for patients in intensive care units is a big challenge. Many severity scores have been proposed, but findings of validation studies have shown that they are not adequately calibrated. The Super ICU Learner Algorithm (SICULA), an ensemble machine learning technique that uses multiple learning algorithms to obtain better prediction performance, does at least as well as the best member of its library. We aimed to assess whether the Super Learner could provide a new mortality prediction algorithm for patients in intensive care units, and to assess its performance compared with other scoring systems.

Authors

  • Romain Pirracchio
  • Maya L Petersen
  • Marco Carone
    Department of Biostatistics, University of Washington.
  • Matthieu Resche Rigon
    Service de Biostatistique et Information Médicale, Unité INSERM 1153, Equipe ECSTRA, Hôpital Saint Louis, Paris, France.
  • Sylvie Chevret
    Service de Biostatistique et Information Médicale, Unité INSERM 1153, Equipe ECSTRA, Hôpital Saint Louis, Paris, France.
  • Mark J van der Laan
    Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.

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