Building Bayesian Networks from Causal Rules.

Bayesian Networks (BNs) are often used for designing diagnosis decision support systems. They are a well-established method for reasoning under uncertainty and making inferences. But, eliciting the probabilities can be tedious and time-consuming especially in medical domain where variables are often related by qualitative terms rather than probabilities. The goal of this paper is to propose a method for eliciting the probabilities required in BNs by using and transforming causal rules which are often used in medicine. The method consists in first constructing the structure of BNs by reporting medical expert's knowledge in the form of causal rules, and then constructing the parameters of the BNs by transforming the terms used for qualified causal rules into probabilities. Example is given in obesity domain. Further works are needed to reinforce our method like the consideration of circular causal rules.