A systematic review on AI/ML approaches against COVID-19 outbreak
A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML...
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| Veröffentlicht in: | Complex & Intelligent Systems 2021-10, Vol.7 (5), p.2655-2678 |
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| creator | Dogan, Onur Tiwari, Sanju Jabbar, M. A. Guggari, Shankru |
| description | A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML. |
| doi_str_mv | 10.1007/s40747-021-00424-8 |
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Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. 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A.</creatorcontrib><creatorcontrib>Guggari, Shankru</creatorcontrib><title>A systematic review on AI/ML approaches against COVID-19 outbreak</title><title>Complex & Intelligent Systems</title><addtitle>Complex Intell. Syst</addtitle><addtitle>Complex Intell Systems</addtitle><description>A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. 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| subjects | Analysis Artificial intelligence Canada China Complexity Computational Intelligence Coronaviruses COVID-19 Criteria Data Structures and Information Theory Datasets Disease transmission Engineering Epidemics Geospatial data India Literature reviews Machine learning Original Original Article Outbreaks Questions Risk assessment Vaccines Viral diseases |
| title | A systematic review on AI/ML approaches against COVID-19 outbreak |
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