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

Diagnosing ventilator-associated pneumonia in mechanically ventilated patients in intensive care units is currently 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 was designed with the help of domain experts. A major limitation of this network is its inability to represent pneumonia as a dynamic process that progresses over time. In this paper, we construct a dynamic Bayesian network that explicitly captures the development of the disease through time. We discuss how probability elicitation from domain experts serves to quantify the dynamics involved and show how the nature of patient data helps reduce the computational burden of inference. We evaluate the diagnostic performance of our dynamic model and report promising results.