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

Bayesian networks constitute a promising tool for COVID-19 outcome prediction in resource-limited settings, combining competitive predictive performance with essential clinical interpretability. The probabilistic approach enables rigorous uncertainty quantification critical for medical decision-making. While the model achieved excellent performance on our cohort, external validation across different populations and SARS-CoV-2 variants is needed to establish broader generalizability.