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Titel
Capturing the Complexity of COVID-19 Research: Trend Analysis in the First Two Years of the Pandemic Using a Bayesian Probabilistic Model and Machine Learning Tools
Autor(es)
Schlagwort
COVID-19
topic modeling
latent Dirichlet allocation
machine learning
text mining
Clasificación UNESCO
1209 Estadística
Fecha de publicación
2022
Resumen
[EN]Publications about COVID-19 have occurred practically since the first outbreak. Therefore,
studying the evolution of the scientific publications on COVID-19 can provide us with information
on current research trends and can help researchers and policymakers to form a structured view
of the existing evidence base of COVID-19 and provide new research directions. This growth rate
was so impressive that the need for updated information and research tools become essential to
mitigate the spread of the virus. Therefore, traditional bibliographic research procedures, such as
systematic reviews and meta-analyses, become time-consuming and limited in focus. This study
aims to study the scientific literature on COVID-19 that has been published since its inception and
to map the evolution of research in the time range between February 2020 and January 2022. The
search was carried out in PubMed extracting topics using text mining and latent Dirichlet allocation
modeling and a trend analysis was performed to analyze the temporal variations in research for each
topic. We also study the distribution of these topics between countries and journals. 126,334 peerreviewed
articles and 16 research topics were identified. The countries with the highest number of
scientific publications were the United States of America, China, Italy, United Kingdom, and India,
respectively. Regarding the distribution of the number of publications by journal, we found that
of the 7040 sources Int. J. Environ. Res. Public Health, PLoS ONE, and Sci. Rep., were the ones that
led the publications on COVID-19. We discovered a growing tendency for eight topics (Prevention,
Telemedicine, Vaccine immunity, Machine learning, Academic parameters, Risk factors and morbidity
and mortality, Information synthesis methods, and Mental health), a falling trend for five of them
(Epidemiology, COVID-19 pathology complications, Diagnostic test, Etiopathogenesis, and Political
and health factors), and the rest varied throughout time with no discernible patterns (Therapeutics,
Pharmacological and therapeutic target, and Repercussion health services).
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