Background: Bayesian Networks (BNs) are probabilistic graphical models that leverage Bayes' theorem to portray dependencies and cause-and-effect relationships between variables. These networks have gained prominence in the field of health sciences, particularly in diagnostic processes, by allowing the integration of medical knowledge into models and addressing uncertainty in a probabilistic manner. Objectives: This review aims to provide an exhaustive overview of the current state of Bayesian Networks in disease diagnosis and prognosis. Additionally, it seeks to introduce readers to the fundamental methodology of BNs, emphasizing their versatility and applicability across varied medical domains. Methods: Employing a meticulous search strategy with MeSH descriptors in diverse scientific databases, we identified 190 relevant references. These were subjected to a rigorous analysis, resulting in the retention of 60 papers for an in-depth review. The robustness of our approach minimizes the risk of selection bias. Results: The selected studies encompass a wide range of medical areas, providing insights into the statistical methodology, implementation feasibility, and predictive accuracy of BNs, as evidenced by an average AUC exceeding 75%. The comprehensive analysis underscores the adaptability and efficacy of Bayesian Networks in diverse clinical scenarios. Conclusion: The encompassing exploration of Bayesian Networks presented in this review highlights their significance and growing impact in the realm of disease diagnosis and prognosis. It underscores the need for further research and development to optimize their capabilities and broaden their applicability in addressing diverse and intricate healthcare challenges.

翻译：背景：贝叶斯网络是一种基于贝叶斯定理的概率图模型，能够刻画变量间的依赖关系与因果关系。该网络通过将医学知识融入模型并以概率方式处理不确定性，在健康科学领域，特别是诊断过程中日益受到重视。目的：本综述旨在全面概述贝叶斯网络在疾病诊断与预后领域的研究现状，同时向读者介绍贝叶斯网络的基本方法论，强调其在多种医学领域中的多功能性与适用性。方法：采用严谨的检索策略，使用医学主题词表（MeSH）描述符在多个科学数据库中进行检索，共识别出190篇相关文献。经严格分析筛选后，最终保留60篇论文进行深度综述。本方法的稳健性有效降低了选择偏倚风险。结果：所选研究涵盖广泛的医学领域，揭示了贝叶斯网络的统计方法学、实施可行性及预测准确性——平均AUC超过75%。综合分析凸显了贝叶斯网络在多样化临床场景中的适应性与效能。结论：本综述对贝叶斯网络的全方位探究强调了其在疾病诊断与预后领域的重要性与日益增长的影响力，同时指出需进一步研究与开发以优化其能力、拓展其适用性，从而应对多样且复杂的医疗挑战。