Data-driven analysis of blood glucose management effectiveness

  • Authors:

  • Barry Nannings;Ameen Abu-Hanna;Robert-Jan Bosman

  • Affiliations:

  • Department of Medical Informatics, Academic Medical Center, Universiteit van Amsterdam, Amsterdam, The Netherlands;Department of Medical Informatics, Academic Medical Center, Universiteit van Amsterdam, Amsterdam, The Netherlands;Department of Intensive Care, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands

  • Venue:
  • AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
  • Year:
  • 2005

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Abstract

The blood-glucose-level (BGL) of Intensive Care (IC) patients requires close monitoring and control. In this paper we describe a general data-driven analytical method for studying the effectiveness of BGL management. The method is based on developing and studying a clinical outcome reflecting the effectiveness of treatment in time. Decision trees are induced in order to discover relevant patient and other characteristics for influencing this outcome. By systematically varying the start and duration of time intervals in which the outcome behavior is studied, our approach distinguishes between time-related (e.g. the BGL at admission time) and intrinsic-related characteristics (e.g. the patient being diabetic).