Uncovering hidden and complex relations of pandemic dynamics using an AI driven system

Demirbaga, Umit; Kaur, Navneet; Aujla, Gagangeet Singh

Abstract

The COVID-19 pandemic continues to challenge healthcare systems globally, necessitating advanced tools for clinical decision support. Amidst the complexity of COVID-19 symptomatology and disease severity prediction, there is a critical need for robust decision support systems to aid healthcare professionals in timely and informed decision-making. In response to this pressing demand, we introduce BayesCovid, a novel decision support system integrating Bayesian network models and deep learning techniques. BayesCovid automates data preprocessing and leverages advanced computational methods to unravel intricate patterns in COVID-19 symptom dynamics. By combining Bayesian networks and Bayesian deep learning models, BayesCovid offers a comprehensive solution for uncovering hidden relationships between symptoms and predicting disease severity. Experimental validation demonstrates BayesCovid ’s high prediction accuracy (83.52–98.97%). Our work represents a significant stride in addressing the urgent need for clinical decision support systems tailored to the complexities of managing COVID-19 cases. By providing healthcare professionals with actionable insights derived from sophisticated computational analysis, BayesCovid aims to enhance clinical decision-making, optimise resource allocation, and improve patient outcomes in the ongoing battle against the COVID-19 pandemic.

Citation

Demirbaga, U., Kaur, N., & Aujla, G. S. (2024). Uncovering hidden and complex relations of pandemic dynamics using an AI driven system. Scientific Reports, 14(1), Article 15433. https://doi.org/10.1038/s41598-024-65845-0

• Journal Article Type: Article
• Acceptance Date: Jun 25, 2024
• Online Publication Date: Jul 4, 2024
• Publication Date: Jul 4, 2024
• Deposit Date: Jul 10, 2024
• Publicly Available Date: Jul 10, 2024
• Journal: Scientific Reports
• Electronic ISSN: 2045-2322
• Publisher: Nature Research
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 1
• Article Number: 15433
• DOI: https://doi.org/10.1038/s41598-024-65845-0
• Public URL: https://durham-repository.worktribe.com/output/2520500