Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm
With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhance...
Gespeichert in:
Veröffentlicht in: | Scientific programming 2017-01, Vol.2017 (2017), p.1-18 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 18 |
---|---|
container_issue | 2017 |
container_start_page | 1 |
container_title | Scientific programming |
container_volume | 2017 |
creator | Zhong, Hongye Xiao, Jitian |
description | With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big health data. Based on the proposed framework, an experimental system is developed as an illustration for the framework implementation. |
doi_str_mv | 10.1155/2017/1901876 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2010877053</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2010877053</sourcerecordid><originalsourceid>FETCH-LOGICAL-c360t-2fe498915c7275e46cd8e85198cc9c9ed1c5d5d6f5289b7ce458b4805e42b07e3</originalsourceid><addsrcrecordid>eNqF0N9LwzAQB_AgCs7pm88S8FHrkrZpkkfdDycMHUPBt5ol1y1za2fSOfzvTenAR5_uOD53B1-ELim5o5SxXkwo71FJqODZEeqEwiJJ5ftx6AkTkYzT9BSdeb8iwVBCOuhjWC5VqW25wGNQ63qJZ9Z_4qkDY3VtqxLvbRgOALZ4AsqVjQzTB7vAA1UrrEqDZ_BtPRg82vlm47kygKfKKWMXm3N0Uqi1h4tD7aK30fC1P44mL49P_ftJpJOM1FFcQCqFpEzzmDNIM20ECEal0FpqCYZqZpjJChYLOecaUibmqSCBxnPCIemi6_bu1lVfO_B1vqp2rgwv8xALEZwTlgR12yrtKu8dFPnW2Y1yPzkleZNhg3l-yDDwm5YvbWnU3v6nr1oNwUCh_nRMkoTI5BeLrnlp</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2010877053</pqid></control><display><type>article</type><title>Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm</title><source>Wiley Online Library Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Alma/SFX Local Collection</source><creator>Zhong, Hongye ; Xiao, Jitian</creator><contributor>Risi, Michele ; Michele Risi</contributor><creatorcontrib>Zhong, Hongye ; Xiao, Jitian ; Risi, Michele ; Michele Risi</creatorcontrib><description>With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big health data. Based on the proposed framework, an experimental system is developed as an illustration for the framework implementation.</description><identifier>ISSN: 1058-9244</identifier><identifier>EISSN: 1875-919X</identifier><identifier>DOI: 10.1155/2017/1901876</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Bayesian analysis ; Data analysis ; Data integration ; Data management ; Deep learning ; Electronic devices ; Health ; Machine learning ; Multisensor fusion ; Neural networks</subject><ispartof>Scientific programming, 2017-01, Vol.2017 (2017), p.1-18</ispartof><rights>Copyright © 2017 Hongye Zhong and Jitian Xiao.</rights><rights>Copyright © 2017 Hongye Zhong and Jitian Xiao.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-2fe498915c7275e46cd8e85198cc9c9ed1c5d5d6f5289b7ce458b4805e42b07e3</citedby><cites>FETCH-LOGICAL-c360t-2fe498915c7275e46cd8e85198cc9c9ed1c5d5d6f5289b7ce458b4805e42b07e3</cites><orcidid>0000-0003-3393-4590</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><contributor>Risi, Michele</contributor><contributor>Michele Risi</contributor><creatorcontrib>Zhong, Hongye</creatorcontrib><creatorcontrib>Xiao, Jitian</creatorcontrib><title>Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm</title><title>Scientific programming</title><description>With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big health data. Based on the proposed framework, an experimental system is developed as an illustration for the framework implementation.</description><subject>Bayesian analysis</subject><subject>Data analysis</subject><subject>Data integration</subject><subject>Data management</subject><subject>Deep learning</subject><subject>Electronic devices</subject><subject>Health</subject><subject>Machine learning</subject><subject>Multisensor fusion</subject><subject>Neural networks</subject><issn>1058-9244</issn><issn>1875-919X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><recordid>eNqF0N9LwzAQB_AgCs7pm88S8FHrkrZpkkfdDycMHUPBt5ol1y1za2fSOfzvTenAR5_uOD53B1-ELim5o5SxXkwo71FJqODZEeqEwiJJ5ftx6AkTkYzT9BSdeb8iwVBCOuhjWC5VqW25wGNQ63qJZ9Z_4qkDY3VtqxLvbRgOALZ4AsqVjQzTB7vAA1UrrEqDZ_BtPRg82vlm47kygKfKKWMXm3N0Uqi1h4tD7aK30fC1P44mL49P_ftJpJOM1FFcQCqFpEzzmDNIM20ECEal0FpqCYZqZpjJChYLOecaUibmqSCBxnPCIemi6_bu1lVfO_B1vqp2rgwv8xALEZwTlgR12yrtKu8dFPnW2Y1yPzkleZNhg3l-yDDwm5YvbWnU3v6nr1oNwUCh_nRMkoTI5BeLrnlp</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Zhong, Hongye</creator><creator>Xiao, Jitian</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-3393-4590</orcidid></search><sort><creationdate>20170101</creationdate><title>Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm</title><author>Zhong, Hongye ; Xiao, Jitian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c360t-2fe498915c7275e46cd8e85198cc9c9ed1c5d5d6f5289b7ce458b4805e42b07e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Bayesian analysis</topic><topic>Data analysis</topic><topic>Data integration</topic><topic>Data management</topic><topic>Deep learning</topic><topic>Electronic devices</topic><topic>Health</topic><topic>Machine learning</topic><topic>Multisensor fusion</topic><topic>Neural networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhong, Hongye</creatorcontrib><creatorcontrib>Xiao, Jitian</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Scientific programming</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhong, Hongye</au><au>Xiao, Jitian</au><au>Risi, Michele</au><au>Michele Risi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm</atitle><jtitle>Scientific programming</jtitle><date>2017-01-01</date><risdate>2017</risdate><volume>2017</volume><issue>2017</issue><spage>1</spage><epage>18</epage><pages>1-18</pages><issn>1058-9244</issn><eissn>1875-919X</eissn><abstract>With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big health data. Based on the proposed framework, an experimental system is developed as an illustration for the framework implementation.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2017/1901876</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0003-3393-4590</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1058-9244 |
ispartof | Scientific programming, 2017-01, Vol.2017 (2017), p.1-18 |
issn | 1058-9244 1875-919X |
language | eng |
recordid | cdi_proquest_journals_2010877053 |
source | Wiley Online Library Open Access; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
subjects | Bayesian analysis Data analysis Data integration Data management Deep learning Electronic devices Health Machine learning Multisensor fusion Neural networks |
title | Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T19%3A13%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Enhancing%20Health%20Risk%20Prediction%20with%20Deep%20Learning%20on%20Big%20Data%20and%20Revised%20Fusion%20Node%20Paradigm&rft.jtitle=Scientific%20programming&rft.au=Zhong,%20Hongye&rft.date=2017-01-01&rft.volume=2017&rft.issue=2017&rft.spage=1&rft.epage=18&rft.pages=1-18&rft.issn=1058-9244&rft.eissn=1875-919X&rft_id=info:doi/10.1155/2017/1901876&rft_dat=%3Cproquest_cross%3E2010877053%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2010877053&rft_id=info:pmid/&rfr_iscdi=true |