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混合效果添加贝叶斯网络用于评估破裂的内动脉瘤:从多中心数据的洞察力

Matteo Delucchi¹, Philippe Bijlenga², Sandrine Morel³

¹Centre for Computational Health, Institute of Computational Life Sciences, Zurich University of Applied Sciences, Wädenswil, Switzerland; Department of Mathematical Modeling and Machine Learning, University of Zurich, Zürich, Switzerland.

Computers in biology and medicine

|December 24, 2025

概括

在多中心研究中考虑中心特定变异对于准确的内动脉瘤 (IA) 风险建模至关重要. 混合效应的附加贝叶斯网络 (ABNs) 通过捕捉研究地点的异质性来提高预测和解释性.

科学领域:

神经外科和神经病学神经外科和神经病学
生物统计学和健康信息学
医学数据科学医学数据科学

背景情况:

多中心临床数据集由于未解决中心特定变异性和复杂的变量依赖性而存在挑战.
现有的内动脉瘤 (IAs) 风险评分往往缺乏准确性和解释性,阻碍了有效的临床应用.
需要先进的分析模型,可以适应多中心数据中的异质性,以改进IA风险评估.

研究的目的:

应用混合效应的添加贝叶斯网络 (ABN) 应用于单独破裂内动脉瘤 (IA) 的大型多中心数据集.
模拟人口统计,临床和动脉瘤特异性特征之间的相互依赖,同时考虑研究中心异质性.
提高IA风险预测模型的精度和可解释性.

主要方法:

采用了来自七个欧洲中心的单独破裂IA的协调,观察,横截面队列.
混合效应的ABNs被采用,模拟概率依赖性使用一般化的线性模型与随机拦截研究中心.
模型性能使用图形比较,类内相关系数 (ICC) 和预测指标进行评估,通过马尔科夫链蒙特卡洛采样进行结构学习.

主要成果:

与聚合模型相比,混合效应的ABN模型显示出更为节的网络结构,有效地捕捉了中心特定的变化.
具有高ICC的变量,例如家族史 (ICC = 0.369),得到了更好的表现.
混合效应的ABN在预测家族史方面表现优于聚合的ABN (AUC = 0.694对比0.585) 并揭示了临床上可解释的关联 (例如,性别,吸烟,IA位置影响IA大小).

结论:

考虑中心间异质性对于在多中心IA群体中准确的风险因子建模至关重要.
混合效应的ABN减少了聚合模型中发现的虚假关联,从而产生了更节和可靠的网络结构.
这种方法增强了异质变量的预测能力,并为IA病理生理学提供了临床可解释的见解,支持个性化风险评估.

关键词:

添加贝叶斯网络添加贝叶斯网络动脉瘤破裂的发生贝叶斯网络是一个贝叶斯网络.阶层模型模型的层次结构.内动脉瘤是一个内动脉瘤.混合效应模型的混合效应模型.多中心的多中心.一个人的内动脉瘤.甲状腺下下垂体出血

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