The complexity of Boolean functions
The complexity of Boolean functions
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Bayesian network modelling through qualitative patterns
Artificial Intelligence
Exploiting causal independence in large Bayesian networks
Knowledge-Based Systems
Causal independence for knowledge acquisition and inference
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Learning symmetric causal independence models
Machine Learning
EM algorithm for symmetric causal independence models
ECML'06 Proceedings of the 17th European conference on Machine Learning
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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.