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Artículo

Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions

Resumen

Since November 2019, the COVID-19 Pandemic produced by Severe Acute Respiratory Syndrome Severe Coronavirus 2 (hereafter COVID-19) has caused approximately seven million deaths globally. Several studies have been conducted using technological tools to prevent infection, to prevent spread, to detect, to vaccinate, and to treat patients with COVID-19. This work focuses on identifying and analyzing machine learning (ML) algorithms used for detection (prediction and diagnosis), monitoring (treatment, hospitalization), and control (vaccination, medical prescription) of COVID-19 and its variants. This study is based on PRISMA methodology and combined bibliometric analysis through VOSviewer with a sample of 925 articles between 2019 and 2022 derived in the prioritization of 32 papers for analysis. Finally, this paper discusses the study’s findings, which are directions for applying ML to address COVID-19 and its variants.
Liu, Qian (58043907500); Yang, Shijie (59167076100); Chen, HeCheng (57203946435)
Global trends and hotspots in the study of the effects of PM2.5 on ischemic stroke
2024
10.1186/s41043-024-00622-3
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202738639&doi=10.1186%2fs41043-024-00622-3&partnerID=40&md5=2f80b4bf91836970169230f4633fc742
Cerebrovascular Disease Department, Gansu Provincial Hospital, No.204 West Donggang Road, Gansu Province, Lanzhou, 730000, China; Key Laboratory of Cerebrovascular Disease of Gansu Province, Gansu Provincial Hospital, Gansu, Lanzhou, China; The First Clinical Medical College of Lanzhou University, Lanzhou, China
All Open Access; Gold Open Access
Scopus
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