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Applications of machine learning approaches to combat COVID-19: A survey
Lessons from COVID-19: Impact on Healthcare Systems and Technology ; : 263-287, 2022.
Article in English | Scopus | ID: covidwho-2027812
ABSTRACT
Machine learning (ML) and artificial intelligence (AI) approaches are prominent and well established in the field of health-care informatics. Because they have a more productive ability to predict, they are successfully applied in several health-care applications. ML approaches are needed thanks to the unsatisfactory experience of the novel virus, considerable ambiguity, complicated social circumstances, and inadequate accessible data. Several approaches have been applied as a tool to combat and protect against the new diseases. The COVID-19 outbreak has rapid growth, so it is not easy to predict the patients and resources within a specified time. ML is a strong approach in the fighting against the pandemic such as COVID-19. It is found significant to predict the susceptible, infected, recovered, or exposed persons and can assist the control strategies to block the spread of infections. This study critically examines the appropriateness and contribution of AI/ML methods on COVID-19 datasets, enhancing the understanding to apply these methods for quick analysis and verification of pandemic databases. © 2022 Elsevier Inc. All rights reserved.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Observational study Language: English Journal: Lessons from COVID-19: Impact on Healthcare Systems and Technology Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Observational study Language: English Journal: Lessons from COVID-19: Impact on Healthcare Systems and Technology Year: 2022 Document Type: Article