Validation of a Large Medical Database

Guido Rovetta *, Patrizia Monteforte *, Gerolamo Bianchi *, 
Stefano Rovetta **, « and Rodolfo Zunino **
University of Genova, Italy
*  Faculty of Medicine, DIMI
** Faculty of Engineering, DIBE


ABSTRACT

Complex clinical problems involving huge experimental
evidence require a preliminary validation of observed data.
This may avoid biasing due to incorrect sampling and
clarify the sample distribution by showing data-inherent
regularities. The paper describes the application of
unsupervised models of neural networks to the analysis
of a very large set of clinical records for the study
of Osteoporosis. The main result obtained lies in showing
the overall uniformity of the data distribution, which
indicates a correct, unbiased sampling of the consideredpopulation.


SUBJECT TERMS

medical information systems; very large databases; data integrity;
neural nets; unsupervised learning; probability; large medical
database; database validation; clinical problems; experimental
evidence; observed data validation; biasing; incorrect sampling;
sample distribution; data-inherent regularities; unsupervised models;
neural networks; clinical records analysis; osteoporosis;
data distribution uniformity; unbiased sampling


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Proceedings of the 8th IEEE Symposium on Computer-Based Medical Systems
Lubbock, Texas, USA, June 1995, pp. 57-64.

Copyright (c) 1995 Institute of Electrical and Electronics Engineers, Inc.
All rights reserved.