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

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

Authors

Umit Demirbaga

Navneet Kaur



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.

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
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

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