Validation of a large medical database

Author

Rovetta, Guido ; Monteforte, Patrizia ; Bianchi, Gerolamo ; Rovetta, Stefano ; Zunino, Rodolfo

Author_Institution

Fac. of Eng, Genova Univ., Italy

fYear

1995

fDate

9-10 Jun 1995

Firstpage

57

Lastpage

64

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 considered population

Keywords

data integrity; medical information systems; neural nets; probability; unsupervised learning; very large databases; biasing; clinical problems; clinical records analysis; data distribution uniformity; data-inherent regularities; database validation; experimental evidence; incorrect sampling; large medical database; neural networks; observed data validation; osteoporosis; sample distribution; unbiased sampling; unsupervised models; Biomedical engineering; Costs; Data engineering; Data mining; Databases; Medical diagnostic imaging; Neural networks; Osteoporosis; Pattern recognition; Sampling methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer-Based Medical Systems, 1995., Proceedings of the Eighth IEEE Symposium on

Conference_Location

Lubbock, TX

Print_ISBN

0-8186-7117-3

Type

conf

DOI

10.1109/CBMS.1995.465447

Filename

465447