Validation of a large medical database

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.<>
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
大型医学数据库的验证
涉及大量实验证据的复杂临床问题需要对观察到的数据进行初步验证。这样可以避免因不正确的采样而产生偏倚,并通过显示数据固有的规律来阐明样本分布。本文描述了应用无监督的神经网络模型来分析一组非常大的骨质疏松症的临床记录。得到的主要结果是显示了数据分布的整体均匀性,这表明对所考虑的总体进行了正确的无偏抽样
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Electronic image management in radiology Enhancement for computed radiographic images A low-cost speech-synthesis system for translation of ASCII text to oral language as a vision impaired aid A study on the knowledge-based thinning algorithm that preserve the shape of the Korean character image Spectroscopic imaging of tissues using micro-endoscopy
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1