Big data analytics for preventive medicine
Identifieur interne : 000313 ( Pmc/Curation ); précédent : 000312; suivant : 000314Big data analytics for preventive medicine
Auteurs : Muhammad Imran Razzak [Australie] ; Muhammad Imran [Arabie saoudite] ; Guandong Xu [Australie]Source :
- Neural Computing & Applications [ 0941-0643 ] ; 2019.
Abstract
Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.
Url:
DOI: 10.1007/s00521-019-04095-y
PubMed: 32205918
PubMed Central: 7088441
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Razzak</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1936 7611</institution-id><institution-id institution-id-type="GRID">grid.117476.2</institution-id><institution>Advanced Analytics Institute,</institution><institution>University of Technology,</institution></institution-wrap>Sydney, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Sydney</wicri:regionArea></affiliation></author><author><name sortKey="Imran, Muhammad" sort="Imran, Muhammad" uniqKey="Imran M" first="Muhammad" last="Imran">Muhammad Imran</name><affiliation wicri:level="1"><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1773 5396</institution-id><institution-id institution-id-type="GRID">grid.56302.32</institution-id><institution>College of Applied Computer Science,</institution><institution>King Saud University,</institution></institution-wrap>Riyadh, Saudi Arabia</nlm:aff><country xml:lang="fr">Arabie saoudite</country><wicri:regionArea>Riyadh</wicri:regionArea></affiliation></author><author><name sortKey="Xu, Guandong" sort="Xu, Guandong" uniqKey="Xu G" first="Guandong" last="Xu">Guandong Xu</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1936 7611</institution-id><institution-id institution-id-type="GRID">grid.117476.2</institution-id><institution>Advanced Analytics Institute,</institution><institution>University of Technology,</institution></institution-wrap>Sydney, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Sydney</wicri:regionArea></affiliation></author></analytic><series><title level="j">Neural Computing & Applications</title><idno type="ISSN">0941-0643</idno><idno type="eISSN">1433-3058</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="Par1">Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Neural Comput Appl</journal-id><journal-id journal-id-type="iso-abbrev">Neural Comput Appl</journal-id><journal-title-group><journal-title>Neural Computing & Applications</journal-title></journal-title-group><issn pub-type="ppub">0941-0643</issn><issn pub-type="epub">1433-3058</issn><publisher><publisher-name>Springer London</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32205918</article-id><article-id pub-id-type="pmc">7088441</article-id><article-id pub-id-type="publisher-id">4095</article-id><article-id pub-id-type="doi">10.1007/s00521-019-04095-y</article-id><article-categories><subj-group subj-group-type="heading"><subject>Cognitive Computing for Intelligent Application and Service</subject></subj-group></article-categories><title-group><article-title>Big data analytics for preventive medicine</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Razzak</surname><given-names>Muhammad Imran</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0002-6946-2591</contrib-id><name><surname>Imran</surname><given-names>Muhammad</given-names></name><address><email>dr.m.imran@ieee.org</email></address><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Xu</surname><given-names>Guandong</given-names></name><address><email>guandong.xu@uts.edu.au</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1936 7611</institution-id><institution-id institution-id-type="GRID">grid.117476.2</institution-id><institution>Advanced Analytics Institute,</institution><institution>University of Technology,</institution></institution-wrap>Sydney, Australia</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1773 5396</institution-id><institution-id institution-id-type="GRID">grid.56302.32</institution-id><institution>College of Applied Computer Science,</institution><institution>King Saud University,</institution></institution-wrap>Riyadh, Saudi Arabia</aff></contrib-group><pub-date pub-type="epub"><day>16</day><month>3</month><year>2019</year></pub-date><fpage>1</fpage><lpage>35</lpage><history><date date-type="received"><day>1</day><month>10</month><year>2018</year></date><date date-type="accepted"><day>12</day><month>2</month><year>2019</year></date></history><permissions><copyright-statement>© Springer-Verlag London Ltd., part of Springer Nature 2019</copyright-statement><license><license-p>This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.</license-p></license></permissions><abstract id="Abs1"><p id="Par1">Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.</p></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Disease prevention</kwd><kwd>Data analytics</kwd><kwd>Healthcare</kwd><kwd>Knowledge discovery</kwd><kwd>Prevention methodologies</kwd></kwd-group><funding-group><award-group><funding-source><institution>Deanship of Scientific Research, King Saud University</institution></funding-source><award-id>RG-1435-051</award-id><principal-award-recipient><name><surname>Imran</surname><given-names>Muhammad</given-names></name></principal-award-recipient></award-group></funding-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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