A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients

Authors:
Theodore Charitos;Linda C. van der Gaag;Stefan Visscher;Karin A. M. Schurink;Peter J. F. Lucas
Affiliations:
Department of Information and Computing Sciences, Utrecht University, The Netherlands;Department of Information and Computing Sciences, Utrecht University, 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;Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Diagnosing ventilator-associated pneumonia in mechanically ventilated patients in intensive care units is seen as a clinical challenge. The difficulty in diagnosing ventilator-associated pneumonia stems from the lack of a simple yet accurate diagnostic test. To assist clinicians in diagnosing and treating patients with pneumonia, a decision-theoretic network had been designed with the help of domain experts. A major limitation of this network is that it does not represent pneumonia as a dynamic process that evolves over time. In this paper, we construct a dynamic Bayesian network that explicitly captures the development of the disease over time. We discuss how probability elicitation from domain experts served to quantify the dynamics involved and how the nature of the patient data helps reduce the computational burden of inference. We evaluate the diagnostic performance of our dynamic model for a number of real patients and report promising results.