title: An Empirical Validation of Recursive Noisy OR (RNOR) Rule for Asthma Prediction.
abstract: In 2004, an extension of the Noisy-OR formalism termed the Recursive Noisy-OR (RNOR) rule was published for estimating complex probabilistic interactions in a Bayesian Network (BN). The RNOR rule presents an algorithm to construct a complete conditional probability distribution (CPD) of a node while allowing domain causal relationships over and above causal independence to be tractably captured in a semantically meaningful way. However, to the best of our knowledge, the accuracy of this rule has not been tested empirically. In this paper, we report the results of a study that compares the performance of a data-trained expert BN (empiric BN) with the reformulated BN, using the RNOR rule. The original empiric BN was trained with a large dataset from the Regenstrief Medical Record System (RMRS). Furthermore, we evaluate conditions in our dataset which render the RNOR rule inapplicable and discuss our use of Noisy-OR calculations in such situations. We call this approach "Adaptive Recursive Noisy-OR".
authors.affiliation: Children's Health Services Research, Indiana University School of Medicine.
authors.forename: Vibha
authors.initials: V
authors.lastname: Anand
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Downs
mesh_terms: D000465:Algorithms; D001249:Asthma; D001499:Bayes Theorem; D006801:Humans; D011336:Probability