AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data
Identifieur interne : 000210 ( Pmc/Checkpoint ); précédent : 000209; suivant : 000211AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data
Auteurs : K. C. SantoshSource :
- Journal of Medical Systems [ 0148-5598 ] ; 2020.
Abstract
The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.
Url:
DOI: 10.1007/s10916-020-01562-1
PubMed: 32189081
PubMed Central: 7087612
Affiliations:
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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Med Syst</journal-id><journal-id journal-id-type="iso-abbrev">J Med Syst</journal-id><journal-title-group><journal-title>Journal of Medical Systems</journal-title></journal-title-group><issn pub-type="ppub">0148-5598</issn><issn pub-type="epub">1573-689X</issn><publisher><publisher-name>Springer US</publisher-name><publisher-loc>New York</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32189081</article-id><article-id pub-id-type="pmc">7087612</article-id><article-id pub-id-type="publisher-id">1562</article-id><article-id pub-id-type="doi">10.1007/s10916-020-01562-1</article-id><article-categories><subj-group subj-group-type="heading"><subject>Education & Training</subject></subj-group></article-categories><title-group><article-title>AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Santosh</surname><given-names>K. C.</given-names></name><address><email>santosh.kc@ieee.org</email></address><xref ref-type="aff" rid="Aff1"></xref></contrib><aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.267169.d</institution-id><institution-id institution-id-type="ISNI">0000 0001 2293 1795</institution-id><institution>Department of Computer Science,</institution><institution>University of South Dakota,</institution></institution-wrap>414 E Clark St, Vermillion, SD 57069 USA</aff></contrib-group><pub-date pub-type="epub"><day>18</day><month>3</month><year>2020</year></pub-date><pub-date pub-type="ppub"><year>2020</year></pub-date><volume>44</volume><issue>5</issue><elocation-id>93</elocation-id><history><date date-type="received"><day>11</day><month>3</month><year>2020</year></date><date date-type="accepted"><day>17</day><month>3</month><year>2020</year></date></history><permissions><copyright-statement>© Springer Science+Business Media, LLC, part of Springer Nature 2020</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">The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.</p></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>COVID-19</kwd><kwd>Artificial intelligence</kwd><kwd>Machine learning</kwd><kwd>Active learning</kwd><kwd>Cross-population train/test models</kwd><kwd>Multitudinal and multimodal data</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© Springer Science+Business Media, LLC, part of Springer Nature 2020</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Santosh, K C" sort="Santosh, K C" uniqKey="Santosh K" first="K. C." last="Santosh">K. C. Santosh</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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