TY - JOUR
T1 - Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions
AU - Salcedo, Dixon
AU - Guerrero, Cesar D.
AU - Saeed, Khalid
AU - Mardini, Johan
AU - Calderon-Benavides, Liliana
AU - Henriquez, Carlos
AU - Mendoza, Andres
N1 - Funding Information:
Partially, this work results from a postdoctoral fellowship, which the Colombia Ministry of Science financed, Technology, and Innovation, within the call “891-2020 MEC. 2- Additional Bank No. 2”. We are also grateful for the financial support of the University of the Coast, and the Autonomous University of Bucaramanga, where we developed the research.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/12/3
Y1 - 2022/12/3
N2 - 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.
AB - 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.
KW - COVID-19
KW - machine learning
KW - mortality prediction
KW - prediction algorithms
UR - https://doi.org/10.3390/electronics11234015
UR - http://www.scopus.com/inward/record.url?scp=85143697204&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/e4e349b7-77be-338b-92a3-55b970fb885c/
U2 - 10.3390/electronics11234015
DO - 10.3390/electronics11234015
M3 - Artículo Científico
AN - SCOPUS:85143697204
SN - 2079-9292
VL - 11
SP - 1
EP - 24
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 23
M1 - 4015
ER -
