Identification of Latent Topics in Patients Surviving COVID-19 in Mexico

Authors

  • Angelica Guzman-Ponce Department of Computer Languages and Systems, Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castelló de la Plana, Spain https://orcid.org/0000-0002-7844-1266
  • Ruben Fernandez-Beltran Departamento de Informática y Sistemas, Universidad de Murcia, Murcia, España. https://orcid.org/0000-0003-1374-8416
  • Rosa Maria Valdovinos-Rosas Facultad de Ingeniería, Universidad Autónoma del Estado de México, Toluca, México https://orcid.org/0000-0001-9954-0653
  • Marcelo Romero-Huertas Facultad de Ingeniería, Universidad Autónoma del Estado de México, Toluca, México https://orcid.org/0000-0002-4758-8484
  • Jose Raymundo Marcial-Romero Facultad de Ingeniería, Universidad Autónoma del Estado de México, Toluca, México https://orcid.org/0000-0002-5808-5727

Keywords:

Latent topics, Latent Dirichlet Allocation (LDA), COVID-19, Risk factors

Abstract

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.

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Author Biographies

Angelica Guzman-Ponce, Department of Computer Languages and Systems, Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castelló de la Plana, Spain

She was conferred a Ph.D. degree in Computer Science, from the Autonomous University of the State of Mexico in 2021. She is a member of the National System of Researchers (CONACYT) Level I. She is currently a postdoctoral research in the University Jaume I (Castellon de la Plana, Spain) and the Universitat Politècnica de València. Her research interests lie in Machine Learning and Graph Theory.

Ruben Fernandez-Beltran, Departamento de Informática y Sistemas, Universidad de Murcia, Murcia, España.

He earned a B.Sc. degree in Computer Science, a M.Sc. in Intelligent Systems and a Ph.D. degree in Computer Science, from the University Jaume I (Castellon de la Plana, Spain) in 2007, 2011 and 2016, respectively. He is currently an Assisstant Professor within the Department of Computer Science and Systems at the University of Murcia, Spain. His research interests lie in multimedia retrieval and spatio-spectral image analysis.

Rosa Maria Valdovinos-Rosas, Facultad de Ingeniería, Universidad Autónoma del Estado de México, Toluca, México

She was conferred a Ph.D. degree in Computer Science. She is a member of the National System of Researchers (CONACYT) Nivel II and the AMEXCOMP. She has been a Full-Time Lecturer-Researcher at the Autonomous University of the State of Mexico (UAEMex). She contributes to the strengthening and consolidation of the scientific community through the training of quality human resources and disseminating knowledge and science in academic-scientific events at a national and international level.

Marcelo Romero-Huertas, Facultad de Ingeniería, Universidad Autónoma del Estado de México, Toluca, México

He was conferred the degree of Philosophy Doctor in Computer Science in 2011 from the University of York (England). He has been a Full-Time Lecturer-Researcher at the Department of Engineering of the Autonomous University of the State of Mexico (UAEMex) since 2011. He is a member of the IEEE, the National System of Researchers (CONACYT) and the Mexican Academy of Computing. His research interest includes image processing and pattern recognition, data network communication protocols.

Jose Raymundo Marcial-Romero, Facultad de Ingeniería, Universidad Autónoma del Estado de México, Toluca, México

He received his PhD in Computational Science from Birmingham University in 2005. He has been a Full-Time Lecturer-Researcher at the Department of Engineering of the Autonomous University of the State of Mexico. He is a member of the National System of Researchers (CONACYT) and Nivel I. His research interest includes approximation theory, computational complexity and graph theory.

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Published

2022-11-04

How to Cite

Guzman-Ponce, A., Fernandez-Beltran, R., Valdovinos-Rosas, R. M., Romero-Huertas, M., & Marcial-Romero, J. R. (2022). Identification of Latent Topics in Patients Surviving COVID-19 in Mexico. IEEE Latin America Transactions, 21(2), 328–334. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6995

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