AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data
Identifieur interne : 000C54 ( Main/Merge ); précédent : 000C53; suivant : 000C55AI-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
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PMC:7087612Le document en format XML
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<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. 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>
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