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Research Papers

Data integration and knowledge discovery in biomedical databases. Reliable information from unreliable sources

Authors:

A Mitnitski ,

Dept of Medicine, Dalhousie University, Halifax, NS B3H 2Y9 Dept of Computer Science, Dalhousie University, Halifax, NS B3H 2Y9
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A Mogilner,

Dept of Medicine, Dalhousie University, Halifax, NS B3H 2Y9
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C MacKnight,

Dept of Medicine, Dalhousie University, Halifax, NS B3H 2Y9
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K Rockwood

Dept of Medicine, Dalhousie University, Halifax, NS B3H 2Y9
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Abstract

To better understand information about human health from databases we analyzed three datasets collected for different purposes in Canada: a biomedical database of older adults, a large population survey across all adult ages, and vital statistics. Redundancy in the variables was established, and this led us to derive a generalized (macroscopic state) variable, being a fitness/frailty index that reflects both individual and group health status. Evaluation of the relationship between fitness/frailty and the mortality rate revealed that the latter could be expressed in terms of variables generally available from any cross-sectional database. In practical terms, this means that the risk of mortality might readily be assessed from standard biomedical appraisals collected for other purposes.
DOI: http://doi.org/10.2481/dsj.2.25
How to Cite: Mitnitski, A. et al., (2003). Data integration and knowledge discovery in biomedical databases. Reliable information from unreliable sources. Data Science Journal. 2, pp.25–34. DOI: http://doi.org/10.2481/dsj.2.25
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Published on 12 Jan 2003.
Peer Reviewed

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