Mixed-effects additive Bayesian networks for the assessment of ruptured intracranial aneurysms: Insights from multicenter data

This application of mixed-effects ABNs reveals that accounting for inter-center heterogeneity is critical for accurately modeling risk factor dependencies in multicenter IA cohorts. This approach yields a more parsimonious network structure by reducing spurious associations found in pooled models. By disentangling patient-level effects from center-specific variations, the model enhances predictive power for heterogeneous variables and provides more reliable, clinically interpretable insights into IA pathophysiology, advancing the potential for personalized risk assessment.