title: A computational model of approximate Bayesian inference for associating clinical algorithms with decision analyses.
abstract: The lack of rationale or explanation is a major deficiency of clinical algorithms. To address this issue, the authors present a computational model for associating decision analyses with clinical algorithms. Automata theory is used to model categorical reasoning with approximate Bayesian inference based on probability intervals. This approximation reduces the number of computations to linear-order instead of the exponential-order combinations of clinical findings in exact Bayes. The linkage of decision analyses and clinical algorithms by means of this model exploits a new concept of "regular" clinical algorithms and their equivalency in theory and provides valuable perspectives in practice for developers of clinical algorithms.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Brigham and Women's Hospital.
authors.forename: I R
authors.initials: IR
authors.lastname: Kamae
authors.forename: R A
authors.initials: RA
authors.lastname: Greenes
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003661:Decision Support Techniques; D003933:Diagnosis; D015233:Models, Statistical