title: Using sensitivity analysis for efficient quantification of a belief network.
abstract: Sensitivity analysis is a method to investigate the effects of varying a model's parameters on its predictions. It was recently suggested as a suitable means to facilitate quantifying the joint probability distribution of a Bayesian belief network. This article presents practical experience with performing sensitivity analyses on a belief network in the field of medical prognosis and treatment planning. Three network quantifications with different levels of informedness were constructed. Two poorly-informed quantifications were improved by replacing the most influential parameters with the corresponding parameter estimates from the well-informed network quantification; these influential parameters were found by performing one-way sensitivity analyses. Subsequently, the results of the replacements were investigated by comparing network predictions. It was found that it may be sufficient to gather a limited number of highly-informed network parameters to obtain a satisfying network quantification. It is therefore concluded that sensitivity analysis can be used to improve the efficiency of quantifying a belief network.
authors.affiliation: Center for Clinical Decision Sciences, Department of Public Health, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands. coupe@mgz.fgg.eur.nl
authors.forename: V M
authors.initials: VM
authors.lastname: Coupé
authors.forename: N
authors.initials: N
authors.lastname: Peek
authors.forename: J
authors.initials: J
authors.lastname: Ottenkamp
authors.forename: J D
authors.initials: JD
authors.lastname: Habbema
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006345:Heart Septal Defects, Ventricular / Q000175:diagnosis / Q000473:pathology; D006352:Heart Ventricles / Q000473:pathology; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer