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

Diagnosing ventilator-associated pneumonia in mechanically ventilated patients ill 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 experts. A major limitation of this network is that it does not represent pneumonia as a dynamic process that evolves over time. fit 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 quantity the dynamics involved and flow 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. (c) 2007 Published by Elsevier Ltd.