Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
The Effects of Disregarding Test Characteristics in Probabilistic Networks
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Probabilities for a probabilistic network: a case study in oesophageal cancer
Artificial Intelligence in Medicine
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Background:: In the medical domain, establishing a diagnosis typically amounts to reasoning about the unobservable truth, based upon a set of indirect observations from diagnostic tests. A diagnostic test may not be perfectly reliable, however. To avoid misdiagnosis, therefore, the reliability characteristics of the test should be taken into account upon reasoning. Objective:: In this paper, we address the issue of modelling the reliability characteristics of diagnostic tests in a probabilistic network. Method:: To this end, we study the mathematical foundation of a test's characteristics and collate them with the probabilities required for a probabilistic network. Results:: We show that the standard reliability characteristics that are generally available from the literature have to be further detailed and stratified, for example by experts, before they can be included in a network. We demonstrate these modelling issues by means of a real-life probabilistic network in oncology.