Abstract:
New classification criteria for vasculitic disorders have recently been proposed by
the American College of Rheumatology. These classification criteria have limitations
inherent to the method employed in their development. We propose a different approach
to the quantitative analysis of the manifestations of vasculitis, which may improve
the precision of classification criteria in this domain. Bayesian classifiers were
developed for six vasculitides using literature-derived quantitative descriptions
of these syndromes. These clinical data were also used in computer programs designed
to generate simulations of vasculitis and control cases. The performance of Bayesian
classifiers of vasculitis was then compared to that of the American College of Rheumatology
criteria, using series of computer-simulated vasculitis cases. Bayesian classifiers
identified simulated vasculitis cases with greater accuracy than those of the corresponding
American College of Rheumatology 1990 vasculitis criteria in all six diseases studied.
As predicted by theoretical considerations, Bayesian classifiers have the potential
to identify vasculitis cases more accurately than the proposed American College of
Rheumatology 1990 criteria.
Keywords:
Vasculitis - Disease Classification - Decision Support - Bayes Theorem - Computer
Simulation
