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A Classification-Tree Hybrid Method for Studying Prognostic Models in Intensive Care

  • Ameen Abu-Hanna
  • Nicolette de Keizer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2101)

Abstract

Health care effectiveness and efficiency are under constant scrutiny especially when treatment is quite costly as in Intensive Care (IC). At the heart of quality of care programs lie prognostic models whose predictions for a particular patient population may be used as a norm to which actual outcomes of that population can be compared. This paper motivates and suggests a method based on Machine Learning and Statistical ideas to study the behavior of current IC prognostic models for predicting in-hospital mortality. An application of this method to an exemplary logistic regression model developed on the IC data from the National Intensive Care Evaluation registry reveals the model’s weaknesses and suggests ways for developing improved prognostic models.

Keywords

Prognostic Model Brier Score Admission Type National Intensive Care Evaluation Probabilistic Logistic Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ameen Abu-Hanna
    • 1
  • Nicolette de Keizer
    • 1
  1. 1.Department of Medical InformaticsAMC-University of AmsterdamAmsterdamThe Netherlands

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