在COVID-19大流行背景下应用贝叶斯网络帮助医生诊断呼吸系统疾病

Ernesto Ocampo Edye, Juan Francisco Kurucz, Lucas Lois, Agustín Paredes, Francisco Piria, Josefina Rodríguez, Silvia Herrera Delgado
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引用次数: 1

摘要

呼吸道疾病的鉴别诊断通常是一线医疗专家面临的一项挑战,在当前的COVID-19大流行下,这一挑战更大。正在使用贝叶斯网络开发临床决策支持系统(cdss),以帮助医生诊断呼吸系统疾病,包括与COVID-19相关的疾病。网络结构是从专家医生那里得到的,网络参数(疾病流行、症状、发现和实验室结果条件概率)是从相关书目或当前标准的全球信息源中提取的。CDSS正在使用来自实际情况的案例研究进行测试,这些案例研究由医生提供和验证。由此产生的系统证明了bn在诊断支持和保健培训方面的适用性和灵活性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying Bayesian Networks to help Physicians Diagnose Respiratory Diseases in the context of COVID-19 Pandemic
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.
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