Discovery and Integration of Organ-Failure Episodes in Mortality Prediction

  • Authors:

  • Tudor Toma;Ameen Abu-Hanna;Robert-Jan Bosman

  • Affiliations:

  • Academic Medical Center, Universiteit van Amsterdam, Department of Medical Informatics, P.O. Box 22700, 1100 DE Amsterdam, The Netherlands;Academic Medical Center, Universiteit van Amsterdam, Department of Medical Informatics, P.O. Box 22700, 1100 DE Amsterdam, The Netherlands;Department of Intensive Care, Onze Lieve Vrouwe Gasthuis, 1e Oosterparkstraat 279, P.O. box 10550, 1090 HM Amsterdam, The Netherlands

  • Venue:
  • AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
  • Year:
  • 2007

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Abstract

Current predictive models in the intensive care rely on summaries of data collected at patient admission. It has been shown recently that temporal patterns of the daily Sequential Organ Failure Assessment (SOFA) scores can improve predictions. However, the derangement of the six individual organ systems underlying the calculation of a SOFA score were not taken into account, thus impeding the understanding of their prognostic merits. In this paper we propose a method for model induction that integrates in a novel way the individual organ failure scores with SOFA scores. The integration of these two correlated components is achieved by summarizing the historic SOFA information and at the same time by capturing the evolution of individual organ system failure status. The method also explicitly avoids the collinearity problem among organ failure episodes. We report on the application of our method to a large dataset and demonstrate its added value. The ubiquity of severity scores and sub-scores in medicine renders our approach relevant to a wide range of medical domains.