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首页> 外文会议>Conference on Artificial Intelligence in Medicine >Bayesian Network vs. Cox's Proportional Hazard Model of PAH Risk: A Comparison
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Bayesian Network vs. Cox's Proportional Hazard Model of PAH Risk: A Comparison

机译：贝叶斯网络与Cox的PAH风险比例风险模型：比较

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摘要

Pulmonary arterial hypertension (PAH) is a severe and often deadly disease, originating from an increase in pulmonary vascular resistance. The REVEAL risk score calculator [3] has been widely used and extensively validated by health-care professionals to predict PAH risks. The calculator is based on the Cox's Proportional Hazard (CPU) model, a popular statistical technique used in risk estimation and survival analysis. In this study, we explore an alternative approach to the PAH patient risk assessment based on a Bayesian network (BN) model using the same variables and discretization cut points as the REVEAL risk score calculator. We applied a Tree Augmented Naieve Bayes algorithm for structure and parameter learning from a data set of 2,456 adult patients from the REVEAL registry. We compared our BN model against the original CPH-based calculator quantitatively and qualitatively. Our BN model relaxes some of the CPH model assumptions, which seems to lead to a higher accuracy (AUC = 0.77) than that of the original calculator (AUG = 0.71). We show that hazard ratios, expressing strength of influence in the CPH model, are static and insensitive to changes in context, which limits applicability of the CPH model to personalized medical care.
机译：肺动脉高压（PAH）是一种严重的疾病，通常是致命的疾病，源于肺血管阻力的增加。 REVEAL风险评分计算器[3]已被医疗保健专业人员广泛使用并得到广泛验证，可以预测PAH风险。该计算器基于Cox的比例危害（CPU）模型，该模型是一种用于风险评估和生存分析的流行统计技术。在这项研究中，我们探索了一种基于贝叶斯网络（BN）模型的PAH患者风险评估的替代方法，该模型使用与REVEAL风险评分计算器相同的变量和离散化切入点。我们从REVEAL注册中心的2456名成年患者的数据集中应用了树增强Naieve Bayes算法进行结构和参数学习。我们将BN模型与基于CPH的原始计算器进行了定量和定性的比较。我们的BN模型放宽了一些CPH模型假设，这似乎导致比原始计算器（AUG = 0.71）更高的精度（AUC = 0.77）。我们表明，在CPH模型中表达影响强度的危险比是静态的，并且对上下文的变化不敏感，这限制了CPH模型对个性化医疗的适用性。

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