Publicação
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
| dc.contributor.author | De la Hoz-M, Javier | |
| dc.contributor.author | Mendes, Susana | |
| dc.contributor.author | Fernández-Gó, María Joséez | |
| dc.contributor.author | González Silva, Yolanda | |
| dc.date.accessioned | 2023-07-28T15:13:52Z | |
| dc.date.available | 2023-07-28T15:13:52Z | |
| dc.date.issued | 2022 | |
| dc.description | Regarding Susana Mendes, this work was funded by national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., under the project MARE (UIDB/04292/2020 and UIDP/04292/2020) and the project LA/P/0069/2020 granted to the Associate Laboratory ARNET. | pt_PT |
| dc.description.abstract | 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). | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | De La Hoz-M, J.; Mendes, S.; Fernández-Gómez, M.J.; González Silva, Y. 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. Computation 2022, 10, 156. https://doi.org/10.3390/ computation10090156 | pt_PT |
| dc.identifier.doi | 10.3390/ computation10090156 | pt_PT |
| dc.identifier.issn | 2079-3197 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/8711 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.relation | Marine and Environmental Sciences Centre | |
| dc.relation | Marine and Environmental Sciences Centre | |
| dc.relation.publisherversion | https://www.mdpi.com/2079-3197/10/9/156 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | COVID-19; | pt_PT |
| dc.subject | Topic modeling | pt_PT |
| dc.subject | Latent Dirichlet allocation | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Text mining | pt_PT |
| dc.title | 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 | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Marine and Environmental Sciences Centre | |
| oaire.awardTitle | Marine and Environmental Sciences Centre | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04292%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04292%2F2020/PT | |
| oaire.citation.issue | 9 | pt_PT |
| oaire.citation.title | Computation | pt_PT |
| oaire.citation.volume | 10 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | DE LA HOZ-M | |
| person.familyName | Mendes | |
| person.familyName | FERNÁNDEZ-GÓMEZ | |
| person.familyName | González Silva | |
| person.givenName | JAVIER | |
| person.givenName | Susana | |
| person.givenName | MARÍA JOSÉ | |
| person.givenName | Yolanda | |
| person.identifier | 322266 | |
| person.identifier | 1597640 | |
| person.identifier.ciencia-id | 4514-12E2-1FD9 | |
| person.identifier.orcid | 0000-0001-7779-0803 | |
| person.identifier.orcid | 0000-0001-9681-3169 | |
| person.identifier.orcid | 0000-0002-5530-6416 | |
| person.identifier.orcid | 0000-0001-7624-8019 | |
| person.identifier.rid | K-6553-2014 | |
| person.identifier.scopus-author-id | 34976470100 | |
| person.identifier.scopus-author-id | 33568138000 | |
| person.identifier.scopus-author-id | 56400947700 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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