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.

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Publication Details

Journal
Computers in Biology and Medicine
Published
2025-12-23
DOI
https://doi.org/10.1016/j.compbiomed.2025.111380
Primary Topic
Intracranial Aneurysms: Treatment and Complications
Type
article
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article

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

Isabel C. Hostettler, Reinhard Furrer, Sandrine Morel, Georg Spinner et al.
Computers in Biology and Medicine
Intracranial Aneurysms: Treatment and Complications
article

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

Isabel C. Hostettler, Reinhard Furrer, Sandrine Morel, Georg Spinner, Philippe Bijlenga, Svenja Maschke, Maria Wostrack, Marian Christoph Neidert, Matteo Delucchi, Sven Hirsch, David John Werring, Antti Lindgren, Oliver Bozinov, Ynte M. Ruigrok, Mark K. Bakker, Henry Houlden, Bernhard Meyer, Romain Bourcier
article en

Abstract

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.

Computers in Biology and MedicineVol. 201
University of Geneva (CH), Centre National de la Recherche Scientifique (FR), Inserm (FR), Queen Mary University of London (GB), University of Eastern Finland (FI), ZHAW Zurich University of Applied Sciences (CH), University of Zurich (CH), University of St.Gallen (CH), TUM Klinikum (DE), Stroke Association (GB), University Medical Center Utrecht (NL), Kuopio University Hospital (FI), University Hospital of Zurich (CH), University Hospital of Geneva (CH), Institut du Thorax (FR), Ostschweizer Kinderspital (CH), University College London (GB), Technical University of Munich (DE), Geneva College (US)
Good health and well-being
Openalex Percentile: Top 32%
Intracranial Aneurysms: Treatment and Complications
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