Using Machine Learning to Identify Benign Cases with Non-Definitive Biopsy.

Finn Kuusisto, Inês Dutra, Houssam Nassif, Yirong Wu, Molly E Klein, Heather B Neuman, Jude Shavlik, Elizabeth S Burnside
{"title":"Using Machine Learning to Identify Benign Cases with Non-Definitive Biopsy.","authors":"Finn Kuusisto,&nbsp;Inês Dutra,&nbsp;Houssam Nassif,&nbsp;Yirong Wu,&nbsp;Molly E Klein,&nbsp;Heather B Neuman,&nbsp;Jude Shavlik,&nbsp;Elizabeth S Burnside","doi":"10.1109/HealthCom.2013.6720685","DOIUrl":null,"url":null,"abstract":"<p><p>When mammography reveals a suspicious finding, a core needle biopsy is usually recommended. In 5% to 15% of these cases, the biopsy diagnosis is non-definitive and a more invasive surgical excisional biopsy is recommended to confirm a diagnosis. The majority of these cases will ultimately be proven benign. The use of excisional biopsy for diagnosis negatively impacts patient quality of life and increases costs to the healthcare system. In this work, we employ a multi-relational machine learning approach to predict when a patient with a non-definitive core needle biopsy diagnosis need <i>not</i> undergo an excisional biopsy procedure because the risk of malignancy is low.</p>","PeriodicalId":73224,"journal":{"name":"Healthcom. International Conference on e-Health Networking, Applications and Services","volume":"2013 15th","pages":"283-285"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/HealthCom.2013.6720685","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Healthcom. International Conference on e-Health Networking, Applications and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HealthCom.2013.6720685","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

When mammography reveals a suspicious finding, a core needle biopsy is usually recommended. In 5% to 15% of these cases, the biopsy diagnosis is non-definitive and a more invasive surgical excisional biopsy is recommended to confirm a diagnosis. The majority of these cases will ultimately be proven benign. The use of excisional biopsy for diagnosis negatively impacts patient quality of life and increases costs to the healthcare system. In this work, we employ a multi-relational machine learning approach to predict when a patient with a non-definitive core needle biopsy diagnosis need not undergo an excisional biopsy procedure because the risk of malignancy is low.

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
使用机器学习识别非明确活检的良性病例。
当乳房x光检查发现可疑时,通常建议进行核心穿刺活检。在5%至15%的病例中,活检诊断不明确,建议进行更具侵入性的手术切除活检以确认诊断。这些病例中的大多数最终将被证明是良性的。使用切除活检进行诊断会对患者的生活质量产生负面影响,并增加医疗保健系统的成本。在这项工作中,我们采用多关系机器学习方法来预测患有不明确核心针活检诊断的患者何时不需要进行切除活检手术,因为恶性肿瘤的风险较低。
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
MDT-based Intelligent Route Selection for 5G-Enabled Connected Ambulances. Machine Learning Based Autism Spectrum Disorder Detection from Videos. Beats-Per-Minute (BPM): A Microservice-based Platform for the Monitoring of Health Related Data via Activity Trackers Improved persuasive design: Matching personal traits and inducing effortful thinking 4G UAV communication system and hovering height optimization for public safety
×
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