Bayesian networks for predicting clinical outcomes in COVID-19 patients: A retrospective study in a resource-limited setting

Open Access

Peer-reviewed

Research Article

About the Authors

Tombolaza Canut Filamant

Roles Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft

Affiliation Thematic Doctoral School Science, Culture, Society and Development, University of Toamasina, Toamasina, Madagascar

Angelo Fulgence Raherinirina

Roles Conceptualization, Formal analysis, Methodology, Validation, Writing – review & editing

Affiliation Research Center for Mathematics Education, University of Fianarantsoa, Fianarantsoa, Madagascar

André Totohasina

Roles Methodology, Validation, Writing – review & editing

Affiliation Department of Mathematics and Informatics, University of Antsiranana, Antsiranana, Madagascar

The authors have declared that no competing interests exist.

COVID 19
Is the Subject Area "COVID 19" applicable to this article?

Thanks for your feedback.
Forecasting
Is the Subject Area "Forecasting" applicable to this article?

Thanks for your feedback.
Machine learning
Is the Subject Area "Machine learning" applicable to this article?

Thanks for your feedback.
Network analysis
Is the Subject Area "Network analysis" applicable to this article?

Thanks for your feedback.
Virus testing
Is the Subject Area "Virus testing" applicable to this article?

Thanks for your feedback.
Medical risk factors
Is the Subject Area "Medical risk factors" applicable to this article?

Thanks for your feedback.
Low and middle income countries
Is the Subject Area "Low and middle income countries" applicable to this article?

Thanks for your feedback.
SARS CoV 2
Is the Subject Area "SARS CoV 2" applicable to this article?

Thanks for your feedback.