학술논문

Applying Bayesian Networks to help Physicians Diagnose Respiratory Diseases in the context of COVID-19 Pandemic
Document Type
Conference
Source
2021 IEEE URUCON URUCON, 2021 IEEE. :368-371 Nov, 2021
Subject
Engineering Profession
General Topics for Engineers
COVID-19
Training
Uncertainty
Pandemics
Pulmonary diseases
Soft sensors
Medical services
Artificial Intelligence
Clinical Decision Support Systems
Bayesian Networks
respiratory diseases
Language
Abstract
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.