Identification of Latent Topics in Patients Surviving COVID-19 in Mexico
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Otros documentos de la autoría: Guzmán-Ponce, Angélica; Fernandez-Beltran, Ruben; Valdovinos Rosas, Rosa María; Romero, Marcelo; Marcial-Romero, J. Raymundo
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Título
Identification of Latent Topics in Patients Surviving COVID-19 in MexicoAutoría
Fecha de publicación
2023-01-11Editor
IEEECita bibliográfica
A. Guzman-Ponce, R. Fernandez-Beltran, R. M. Valdovinos-Rosas, M. Romero-Huertas and J. R. Marcial-Romero, "Identification of Latent Topics in Patients Surviving COVID-19 in Mexico," in IEEE Latin America Transactions, vol. 21, no. 2, pp. 328-334, Feb. 2023, doi: 10.1109/TLA.2023.10015226.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
With the outbreak of the SARS-CoV-2 o COVID-19 pandemic, multiple studies of risk factors and their influence on patient deaths have been developed. However, little attention is often paid to analyzing patients in ... [+]
With the outbreak of the SARS-CoV-2 o COVID-19 pandemic, multiple studies of risk factors and their influence on patient deaths have been developed. However, little attention is often paid to analyzing patients in risk groups despite the fact that they have been infected and inpatients can survive. In this article, with the dataset available from the Ministery of the health of Mexico, this paper proposes the use of the latent topic extraction algorithm Latent Dirichlet Allocation (LDA) for the study of COVID-19 survival factors in Mexico. The results let us conclude that in the year before strategies for prevention and control of COVID-19, the latent topics support that patients without comorbidities have a low risk of death, compared with the period of 2021, wherein in spite of having some risk factors patients can survive. [-]
Publicado en
IEEE Latin America Transactions, vol. 21, no. 2, Feb. 2023Entidad financiadora
Universidad Autónoma del Estado de Morelos (UAEM), México | Unión EuropeaNextGenerationEU
Código del proyecto o subvención
6364/2021SF | MGS/2021/23(UP2021-021)
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© Copyright 2023 IEEE - All rights reserved.
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