Using Machine Learning to Improve Surgical Outcomes

Sindhura Bonthu, P. Armijo, Tiffany Tanner, Qiuming A. Zhu
{"title":"Using Machine Learning to Improve Surgical Outcomes","authors":"Sindhura Bonthu, P. Armijo, Tiffany Tanner, Qiuming A. Zhu","doi":"10.1109/ICMLA.2019.00233","DOIUrl":null,"url":null,"abstract":"Predicting the severity of patient’s condition helps providing accurate clinical care. Mortality prediction is one of the challenges due to distinct characteristics of the patient’s data. It is a challenging problem to evaluate the patient’s data which is highly sparse, highly biased and imbalanced, and highly mixed. In this paper, we are focusing on processing large volumes of data using neural networks which can be further used for analysis to obtain useful insights, such as identifying the major features contributing to certain outcomes of events or classifying different objects based on the presences of certain attributes and their measurements.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2019.00233","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

Predicting the severity of patient’s condition helps providing accurate clinical care. Mortality prediction is one of the challenges due to distinct characteristics of the patient’s data. It is a challenging problem to evaluate the patient’s data which is highly sparse, highly biased and imbalanced, and highly mixed. In this paper, we are focusing on processing large volumes of data using neural networks which can be further used for analysis to obtain useful insights, such as identifying the major features contributing to certain outcomes of events or classifying different objects based on the presences of certain attributes and their measurements.
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
利用机器学习提高手术效果
预测患者病情的严重程度有助于提供准确的临床护理。由于患者数据的不同特征,死亡率预测是一个挑战。患者数据高度稀疏、高度偏倚和不平衡、高度混杂,对其进行评估是一个具有挑战性的问题。在本文中,我们专注于使用神经网络处理大量数据,这些数据可以进一步用于分析以获得有用的见解,例如识别导致某些事件结果的主要特征,或根据某些属性及其测量值对不同对象进行分类。
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
Automated Stenosis Classification of Carotid Artery Sonography using Deep Neural Networks Hybrid Condition Monitoring for Power Electronic Systems Time Series Anomaly Detection from a Markov Chain Perspective Anyone here? Smart Embedded Low-Resolution Omnidirectional Video Sensor to Measure Room Occupancy Deep Learning with Domain Randomization for Optimal Filtering
×
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