AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data
Identifieur interne : 000658 ( new/Analysis ); précédent : 000657; suivant : 000659AI-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.
Descripteurs français
- KwdFr :
- Algorithmes, Apprentissage machine, Flambées de maladies, Humains, Infections à coronavirus (diagnostic), Infections à coronavirus (épidémiologie), Intelligence artificielle, Pneumopathie virale (diagnostic), Pneumopathie virale (épidémiologie), Prestations des soins de santé, Prise de décision, Prévision.
- MESH :
English descriptors
- KwdEn :
- MESH :
- diagnosis : Coronavirus Infections, Pneumonia, Viral.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- Algorithms, Artificial Intelligence, Decision Making, Delivery of Health Care, Disease Outbreaks, Forecasting, Humans, Machine Learning.
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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PMC:7087612Le document en format XML
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Santosh</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">Journal of Medical Systems</title><idno type="ISSN">0148-5598</idno><idno type="eISSN">1573-689X</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Coronavirus Infections (diagnosis)</term><term>Coronavirus Infections (epidemiology)</term><term>Decision Making</term><term>Delivery of Health Care</term><term>Disease Outbreaks</term><term>Forecasting</term><term>Humans</term><term>Machine Learning</term><term>Pneumonia, Viral (diagnosis)</term><term>Pneumonia, Viral (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Apprentissage machine</term><term>Flambées de maladies</term><term>Humains</term><term>Infections à coronavirus (diagnostic)</term><term>Infections à coronavirus (épidémiologie)</term><term>Intelligence artificielle</term><term>Pneumopathie virale (diagnostic)</term><term>Pneumopathie virale (épidémiologie)</term><term>Prestations des soins de santé</term><term>Prise de décision</term><term>Prévision</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Decision Making</term><term>Delivery of Health Care</term><term>Disease Outbreaks</term><term>Forecasting</term><term>Humans</term><term>Machine Learning</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Apprentissage machine</term><term>Flambées de maladies</term><term>Humains</term><term>Intelligence artificielle</term><term>Prestations des soins de santé</term><term>Prise de décision</term><term>Prévision</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><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. 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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></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Wu, F" uniqKey="Wu F">F Wu</name></author><author><name sortKey="Zhao, S" uniqKey="Zhao S">S Zhao</name></author><author><name sortKey="Yu, B" uniqKey="Yu B">B Yu</name></author><author><name sortKey="Chen, Ym" uniqKey="Chen Y">YM Chen</name></author><author><name sortKey="Wang, W" uniqKey="Wang W">W Wang</name></author><author><name sortKey="Song, Zg" uniqKey="Song Z">ZG Song</name></author><author><name sortKey="Hu, Y" uniqKey="Hu Y">Y Hu</name></author><author><name sortKey="Tao, Zw" uniqKey="Tao Z">ZW Tao</name></author><author><name sortKey="Tian, Jh" uniqKey="Tian J">JH Tian</name></author><author><name sortKey="Pei, Yy" uniqKey="Pei Y">YY Pei</name></author><author><name sortKey="Yuan, Ml" uniqKey="Yuan M">ML Yuan</name></author><author><name sortKey="Zhang, Yl" uniqKey="Zhang Y">YL Zhang</name></author><author><name sortKey="Dai, Fh" uniqKey="Dai F">FH Dai</name></author><author><name sortKey="Liu, Y" uniqKey="Liu Y">Y Liu</name></author><author><name sortKey="Wang, Qm" uniqKey="Wang Q">QM Wang</name></author><author><name sortKey="Zheng, Jj" uniqKey="Zheng J">JJ Zheng</name></author><author><name sortKey="Xu, L" uniqKey="Xu L">L Xu</name></author><author><name sortKey="Holmes, Ec" uniqKey="Holmes E">EC Holmes</name></author><author><name sortKey="Zhang, Yz" uniqKey="Zhang Y">YZ Zhang</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Drosten, C" uniqKey="Drosten C">C Drosten</name></author><author><name sortKey="Gunther, S" uniqKey="Gunther S">S Gunther</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="De Wit, E" uniqKey="De Wit E">E de Wit</name></author><author><name sortKey="Van Doremalen, N" uniqKey="Van Doremalen N">N van Doremalen</name></author><author><name sortKey="Falzarano, D" uniqKey="Falzarano D">D Falzarano</name></author><author><name sortKey="Munster, Vj" uniqKey="Munster V">VJ Munster</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Dewey, M" uniqKey="Dewey M">M Dewey</name></author><author><name sortKey="Schlattmann, P" uniqKey="Schlattmann P">P Schlattmann</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Bouguelia, M" uniqKey="Bouguelia M">M Bouguelia</name></author><author><name sortKey="Nowaczyk, S" uniqKey="Nowaczyk S">S Nowaczyk</name></author><author><name sortKey="Santosh, Kc" uniqKey="Santosh K">KC Santosh</name></author><author><name sortKey="Verikas, A" uniqKey="Verikas A">A Verikas</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Fong, Sj" uniqKey="Fong S">SJ Fong</name></author><author><name sortKey="Li, G" uniqKey="Li G">G Li</name></author><author><name sortKey="Dey, N" uniqKey="Dey N">N Dey</name></author><author><name sortKey="Crespo, Rg" uniqKey="Crespo R">RG Crespo</name></author><author><name sortKey="Herrera Viedma, E" uniqKey="Herrera Viedma E">E Herrera-Viedma</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list></list><tree><noCountry><name sortKey="Santosh, K C" sort="Santosh, K C" uniqKey="Santosh K" first="K. 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