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dc.contributor.authorLin, Rongheng
dc.contributor.authorYe, Zezhou
dc.contributor.authorWang, Hao
dc.contributor.authorWu, Budan
dc.date.accessioned2019-05-03T08:38:42Z
dc.date.available2019-05-03T08:38:42Z
dc.date.created2018-10-26T11:29:41Z
dc.date.issued2018
dc.identifier.citationIEEE Reviews in Biomedical Engineering. 2018, 11 275-288.nb_NO
dc.identifier.issn1941-1189
dc.identifier.urihttp://hdl.handle.net/11250/2596414
dc.description.abstractWith the advancement of technology in data science and network technology, the world has stepped into the Era of Big Data, and the medical field is rich in data suitable for analysis. Thus, in recent years, there has been much research in medical big data, mainly targeting data collection, data analysis, and visualization. However, very few works provide a full survey of the medical big data on chronic diseases and health monitoring. This review investigates recent research efforts and conducts a comprehensive overview of the work on medical big data, especially as related to chronic diseases and health monitoring. It focuses on the full cycles of the big data processing, which includes medical big data preprocessing, big data tools and algorithms, big data visualization, and security issues in big data. It also attempts to combine common big data technologies with special medical needs by analyzing in detail existing works of medical big data. To the best of our knowledge, this is the first survey that targets chronic diseases and health monitoring big data technologies.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.titleChronic Diseases and Health Monitoring Big Data: A Surveynb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber275-288nb_NO
dc.source.volume11nb_NO
dc.source.journalIEEE Reviews in Biomedical Engineeringnb_NO
dc.identifier.doi10.1109/RBME.2018.2829704
dc.identifier.cristin1623824
dc.description.localcode© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,55,0
cristin.unitnameInstitutt for IKT og realfag
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode0


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