title: Probabilistic asthma case finding: a noisy or reformulation.
abstract: Bayesian Networks are used to model domain knowledge with natural perception of causal influences. Even though Bayesian Networks reduce the number of probabilities required to specify relationships in the domain, specifying these probabilities for large networks can be prohibitive. The Noisy-OR formalism of Bayesian Network (BN) overcomes this shortcoming by making an assumption of causal independence among the modeled causes and their common effect. However, the accuracy of this assumption has rarely been tested. In this paper we report the results of an empirical study in the domain of asthma case finding that compares the Noisy-OR reformulation of the expert BN with the expert BN trained using large clinical data set from the Regenstrief Medical Record System. Our results show that the BN with Noisy-OR formulation for this domain performs comparably with the experts BN suggesting that this formalism is robust, at least in this domain.
authors.affiliation: Children's Health Services Research, Indiana University School of Medicine, Indianapolis, USA.
authors.forename: Vibha
authors.initials: V
authors.lastname: Anand
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Downs
mesh_terms: D000293:Adolescent; D001185:Artificial Intelligence; D001249:Asthma / Q000175:diagnosis* / Q000453:epidemiology*; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D003627:Data Interpretation, Statistical; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D016016:Proportional Hazards Models