Using a Bayesian-Network Model for the Analysis of Clinical Time-Series Data

Time is an essential element in the clinical management of patients as disease processes develop in time. Not surprisingly, the amount of temporal clinical information that is being collected to understand what is happening in the patient is therefore continuously increasing. A typical example of a disease process where time is considered important is the development of ventilator-associated pneumonia (VAP) during the stay in the ICU. A Bayesian network was developed previously to support clinicians in the diagnosis and treatment of VAP in the ICU. 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.