Understanding the temporal evolution of Covid-19 research through machine learning and natural language processing

Datos Bibliográficos

ID21442822
AutoresAshkan Ebadi (0000-0002-4542-9105, National Academies of Sciences, Engineering, and Medicine), Pengcheng Xi (0000-0003-3236-5234, National Research Council Canada), Stéphane Tremblay (0009-0007-0829-5041, National Research Council Canada), Bruce Spencer (0000-0003-1093-4870, University of New Brunswick), Raman Pall (National Research Council Canada), Alexander Wong (0009-0009-9781-5479, University of Waterloo)
Año2021
Volumen126
Número1
Páginas725-739
Fecha de publicación2021-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaScientometrics (JOURNAL)
Identificadores de la revistaISSN: 0138-9130 • E-ISSN: 1588-2861
EditorialSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11192-020-03744-7
PMID33230352
OpenAlexW3045327594
IdiomaEN
Citas recibidas10
Referencias citadas17

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January-May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed

Contagious disease · Coronavirus disease 2019 (COVID-19) · Data science · Disease · Diversity (politics) · Geography · Infectious disease (medical specialty) · Outbreak · Pathology · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) · Similarity (geometry) · Sociology · Artificial Intelligence · Computer Science · COVID-19 diagnosis using AI · Machine Learning in Healthcare · Medicine · Misinformation and Its Impacts · Virology