Techniques for knowledge discovery in existing biomedical databases: estimation of individual aging effects in cognition in relation to dementia.

Full Abstract

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.

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Author information

Author/s: Mitnitski, Arnold B (AB); Mogilner, Alexander J (AJ); Graham, Janice E (JE); Rockwood, Kenneth (K);

Affiliation: Geriatric Medicine Research, Unit Suite 1421, Dalhousie University, 5955 Veterans' Memorial Lane, Halifax, Nova Scotia B3H 2E1, Canada.

Journal and publication information

Publication Type: Journal Article

Journal: Journal of clinical epidemiology (J Clin Epidemiol), published in England. (Language: eng)

Reference: 2003-Feb; vol 56 (issue 2) : pp 116-23

Dates: Created 2003/03/25; Completed 2003/05/23; Revised 2004/11/17;

PMID: 12654405, status: MEDLINE (last retrieval date: 12/26/2008)

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Databases, Factual
Dementia - psychology
Humans
Medical Informatics - methods
Models, Statistical

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