Why Is Diagnosis Using Belief Networks Insensitive to Imprecision In Probabilities?
This addresses the reliability of belief networks in medical diagnosis, showing incremental insights into their robustness to probability errors.
The paper investigates why diagnostic performance using Bayesian belief networks remains robust despite imprecise probabilities, finding that average probability of true diseases shows small sensitivity even to large noise, with effects modest even for asymmetric noise distributions.
Recent research has found that diagnostic performance with Bayesian belief networks is often surprisingly insensitive to imprecision in the numerical probabilities. For example, the authors have recently completed an extensive study in which they applied random noise to the numerical probabilities in a set of belief networks for medical diagnosis, subsets of the CPCS network, a subset of the QMR (Quick Medical Reference) focused on liver and bile diseases. The diagnostic performance in terms of the average probabilities assigned to the actual diseases showed small sensitivity even to large amounts of noise. In this paper, we summarize the findings of this study and discuss possible explanations of this low sensitivity. One reason is that the criterion for performance is average probability of the true hypotheses, rather than average error in probability, which is insensitive to symmetric noise distributions. But, we show that even asymmetric, logodds-normal noise has modest effects. A second reason is that the gold-standard posterior probabilities are often near zero or one, and are little disturbed by noise.