G. Rovetta, P. Monteforte, G. Bianchi, S. Rovetta, R. Zunino
{"title":"Validation of a large medical database","authors":"G. Rovetta, P. Monteforte, G. Bianchi, S. Rovetta, R. Zunino","doi":"10.1109/CBMS.1995.465447","DOIUrl":null,"url":null,"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.<<ETX>>","PeriodicalId":254366,"journal":{"name":"Proceedings Eighth IEEE Symposium on Computer-Based Medical Systems","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Eighth IEEE Symposium on Computer-Based Medical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.1995.465447","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.<>