Using a bayesian-network model for the analysis of clinical time-series data

  • Authors:

  • Stefan Visscher;Peter Lucas;Karin Schurink;Marc Bonten

  • Affiliations:

  • Department of Internal Medicine and Infectious Diseases, University Medical Center Utrecht, The Netherlands;Institute for Computing and Information Sciences, Radboud University Nijmegen, The Netherlands;Department of Internal Medicine and Infectious Diseases, University Medical Center Utrecht, The Netherlands;Department of Internal Medicine and Infectious Diseases, University Medical Center Utrecht, The Netherlands

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

Quantified Score

Hi-index 0.00

Visualization

Abstract

Time is an essential element in the clinical management of patients as disease processes develop in time. A typical example of a disease process where time is considered important is the development of ventilator-associated pneumonia (VAP). A Bayesian network was developed previously to support clinicians in the diagnosis and treatment of VAP. In the research described in this paper, we have investigated whether this Bayesian network can also be used to analyse the temporal data collected in the ICU for patterns indicating development of VAP. In addition, it was studied whether the Bayesian network was able to suggest appropriate antimicrobial treatment. A temporal database with over 17700 patient days was used for this purpose.