Improving the Therapeutic Performance of a Medical Bayesian Network Using Noisy Threshold Models

Abstract

Treatment management in critically ill patients needs to be efficient, as delay in treatment may give rise to deterioration in the patient's condition. Ventilator-associated pneumonia (VAP) occurs in patients who are mechanically ventilated in intensive care units. As it is quite difficult to diagnose and treat VAP, some form of computer-based decision support might be helpful. As diagnosing and treating disorders in medicine involves reasoning with uncertainty, we have used a Bayesian network as our primary tool for building a decision-support system for the clinical management of VAP. The effects of antibiotics on colonisation with various pathogens and subsequent antibiotic choices in case of VAP were modelled in the Bayesian network using the notion of causal independence. In particular, the conditional probability distribution of the random variable that represents the overall coverage of pathogens by antibiotics was modelled in terms of the conjunctive effect of the seven different pathogens, usually referred to as the noisy-AND gate. In this paper, we investigate generalisations of the noisy-AND, called noisy threshold models. It is shown that they offer a means for further improvement to the performance of the Bayesian network.