算法诊断方法综述:大数据方法

A. Kupusinac, R. Doroslovački
{"title":"算法诊断方法综述:大数据方法","authors":"A. Kupusinac, R. Doroslovački","doi":"10.1109/ZINC.2018.8448548","DOIUrl":null,"url":null,"abstract":"Modern medical diagnosis requires the development of algorithms for processing large amounts of data (big data analytics), with numerous complex and unconventional cases, where the analysis with clasical statistical methods is usually inapplicable. Methods of machine learning are good solution, since they can discover complicated relationships from given dataset and than use this knowledge for new data. This paper considers methodology of algorithmic diagnosis based on artificial neural networks.","PeriodicalId":366195,"journal":{"name":"2018 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Overview of the Algorithmic Diagnostics Methodology: A Big Data Approach\",\"authors\":\"A. Kupusinac, R. Doroslovački\",\"doi\":\"10.1109/ZINC.2018.8448548\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Modern medical diagnosis requires the development of algorithms for processing large amounts of data (big data analytics), with numerous complex and unconventional cases, where the analysis with clasical statistical methods is usually inapplicable. Methods of machine learning are good solution, since they can discover complicated relationships from given dataset and than use this knowledge for new data. This paper considers methodology of algorithmic diagnosis based on artificial neural networks.\",\"PeriodicalId\":366195,\"journal\":{\"name\":\"2018 Zooming Innovation in Consumer Technologies Conference (ZINC)\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Zooming Innovation in Consumer Technologies Conference (ZINC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ZINC.2018.8448548\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Zooming Innovation in Consumer Technologies Conference (ZINC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ZINC.2018.8448548","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

现代医学诊断需要开发处理大量数据的算法(大数据分析),其中有许多复杂和非常规的病例,用经典统计方法进行分析通常是不适用的。机器学习的方法是很好的解决方案,因为它们可以从给定的数据集中发现复杂的关系,然后将这些知识用于新数据。本文研究了基于人工神经网络的算法诊断方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Overview of the Algorithmic Diagnostics Methodology: A Big Data Approach
Modern medical diagnosis requires the development of algorithms for processing large amounts of data (big data analytics), with numerous complex and unconventional cases, where the analysis with clasical statistical methods is usually inapplicable. Methods of machine learning are good solution, since they can discover complicated relationships from given dataset and than use this knowledge for new data. This paper considers methodology of algorithmic diagnosis based on artificial neural networks.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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