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New interest is being expressed in the systematic application of modeling techniques to existing datasets. Under the rubric of Knowledge Discovery in Databases (KDD) large databases are being exploited for commercial and scientific purposes. This article reviews the development and applications of KDD techniques to dementia, using the longitudinal Canadian Study of Health and Aging dataset. KDD has demonstrated usefulness at the group level. For example, as in the course of functional impairment between Alzheimer's disease and no cognitive impairment suggest damage control-protection mechanisms for the former compared with noncompensated random accumulation of deficits for the latter. At the individual level, KDD suggests that more precise diagnosis seems possible as well as individual life expectancy prediction. Biomedical databases appear to hold the potential for novel insights when explored by systematic modeling.

In this article, we present the novel approach of using a multi-state model to describe longitudinal changes in cognitive test scores. Scores are modelled according to a truncated Poisson distribution, conditional on survival to a fixed endpoint, with the Poisson mean dependent upon the baseline score and covariates. The model provides a unified treatment of the distribution of cognitive scores, taking into account baseline scores and survival. It offers a simple framework for the simultaneous estimation of the effect of covariates modulating these distributions, over different baseline scores. A distinguishing feature is that this approach permits estimation of the probabilities of transitions in different directions: improvements, declines and death. The basic model is characterised by four parameters, two of which represent cognitive transitions in survivors, both for individuals with no cognitive errors at baseline and for those with non-zero errors, within the range of test scores. The two other parameters represent corresponding likelihoods of death. The model is applied to an analysis of data from the Canadian Study of Health and Aging (1991–2001) to identify the risk of death, and of changes in cognitive function as assessed by errors in the Modified Mini-Mental State Examination. The model performance is compared with more conventional approaches, such as multivariate linear and polytomous regressions. This model can also be readily applied to a wide variety of other cognitive test scores and phenomena which change with age. [ABSTRACT FROM PUBLISHER]

Examines a model on the rate of decline in functions in Alzheimer's disease and in other dementias in comparison with normal aging. Problems with previous methods; Use of an impairment index; Results and discussion.

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. [ABSTRACT FROM AUTHOR]