Vector diagnostics in dementia derived from Bayes' theorem.

This paper introduces the concept of vector diagnostics. In contrast to the conventional approach where one diagnosis takes precedence, the authors propose an alternative strategy that addresses the clinical reality of comorbidity and multiple diagnoses for an individual. Based on a Bayesian approach, the probability distribution for the etiologically heterogeneous dementia diagnoses is estimated from the Canadian Study of Health and Aging database. These data were collected between February 1991 and May 1992. This method facilitates the establishment of a probability for more than one diagnosis within a given individual. By analyzing the correspondence between diagnostic groups, it is demonstrated that some clinical diagnoses are not reliably distinguished on the basis of the considered subset of symptoms and signs. As a consequence, the conventional diagnostic categories might require revision. The resulting probabilistic algorithm allows for the mining of existing epidemiologic databases for patterns of signs and symptoms that characterize emerging diagnostic categories which might better account for the heterogeneity of the dementia subtypes and individual variability.