Modelling Evolution of the Intracranial Aneurysm Disease by Applying Probabilistic Graphical and Machine Learning Models

Discover how interpretable machine learning and probabilistic graphical models are advancing personalized risk assessment in intracranial aneurysm disease. This PhD thesis presents innovative Bayesian network methodologies for analyzing multicenter clinical data, enabling robust and transparent modeling of rupture risk factors. The research bridges statistics and clinical neuroscience, providing new tools for a deeper understanding of stroke.