Applying Bayesian Networks to help Physicians Diagnose Respiratory Diseases in the context of COVID-19 Pandemic
Ernesto Ocampo Edye, Juan Francisco Kurucz, Lucas Lois, Agustin Paredes, Francisco Piria, Josefina Rodriguez, Silvia Herrera Delgado · 2021 IEEE URUCON · 2021
The differential diagnosis of respiratory diseases is usually a challenge for medical specialists in the first line of care, increased under the current COVID-19 pandemic. A Clinical Decision Support System-CDSS - is being developed using Bayesian Networks – BNs – to help physicians diagnose respiratory diseases, including those related to COVID-19. Network structure has been elicited from expert physicians, and network parameters (diseases prevalence, symptoms, findings, and lab results conditional probabilities) were extracted from relevant bibliography or currently standard global information sources. The CDSS is being tested using case studies taken from real situations, provided and validated by physicians. The resulting system demonstrates the suitability and flexibility of BNs for diagnosis support and healthcare training.