The Diagnostic Imagination in Radiology: Part 2.

Radiology management Pub Date : 2017-03-01
Rodney Sappington
{"title":"The Diagnostic Imagination in Radiology: Part 2.","authors":"Rodney Sappington","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Developing algorithms for the improve- ment of diagnostic care leverages tech- nologies and techniques developed across industries that are exponentially being improved, developed, and tested. Machine learning means extracting patterns not only from patient level obser- vations or a radiologist's primary diag- nosis, but from secondary diagnoses, incidental findings, claims data and similarities with other patients for predictive benefit. The business model for radiology will be based on deeply knowing and leveraging existing data and generating data on patients that can be reused and made easily accessible for-future algo- rithms and changes in healthcare policy and reimbursement.</p>","PeriodicalId":74636,"journal":{"name":"Radiology management","volume":"39 2","pages":"39-43"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiology management","FirstCategoryId":"1085","ListUrlMain":"","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

Developing algorithms for the improve- ment of diagnostic care leverages tech- nologies and techniques developed across industries that are exponentially being improved, developed, and tested. Machine learning means extracting patterns not only from patient level obser- vations or a radiologist's primary diag- nosis, but from secondary diagnoses, incidental findings, claims data and similarities with other patients for predictive benefit. The business model for radiology will be based on deeply knowing and leveraging existing data and generating data on patients that can be reused and made easily accessible for-future algo- rithms and changes in healthcare policy and reimbursement.

放射学中的诊断想象:第2部分。
开发用于改进诊断护理的算法利用了跨行业开发的技术和技术,这些技术和技术正以指数方式得到改进、开发和测试。机器学习意味着不仅可以从患者层面的观察或放射科医生的初步诊断中提取模式,还可以从二次诊断、偶然发现、索赔数据以及与其他患者的相似性中提取模式,从而获得预测性收益。放射学的商业模式将基于对现有数据的深入了解和利用,并生成关于患者的数据,这些数据可以重复使用,并且易于访问,以适应未来的算法节奏和医疗保健政策和报销的变化。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信