PDF(Pubmed)
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.
摘要：
COVID-19大流行继续挑战全球医疗保健系统，需要用于临床决策支持的高级工具。在COVID-19症状学和疾病严重程度预测的复杂性中，迫切需要强大的决策支持系统，以帮助医疗保健专业人员及时做出明智的决策。为了应对这一紧迫的需求，我们介绍BayesCovid,集成贝叶斯网络模型和深度学习技术的新型决策支持系统。BayesCovid自动化数据预处理，并利用先进的计算方法来解开COVID-19症状动态中的复杂模式。通过结合贝叶斯网络和贝叶斯深度学习模型,BayesCovid提供了一个全面的解决方案，用于发现症状和预测疾病严重程度之间的隐藏关系。实验验证表明，BayesCovid具有很高的预测精度(83.52-98.97%)。我们的工作代表了在解决迫切需要为管理COVID-19病例的复杂性量身定制的临床决策支持系统方面迈出的重要一步。通过为医疗保健专业人员提供从复杂的计算分析中得出的可行见解，BayesCovid旨在加强临床决策，优化资源分配，并在与COVID-19大流行的持续斗争中改善患者的预后。
