Longitudinal modeling of MS patient trajectories improves predictions of disability progression. - Edward De Brouwer; Thijs Becker; Yves Moreau; Eva Kubala Havrdova; Maria Trojano; Sara Eichau; Serkan Ozakbas; Marco Onofrj; Pierre Grammond; Jens Kuhle; Ludwig Kappos; Patrizia Sola; et al. (CoRR, 2020)

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression. - Edward De Brouwer; Thijs Becker; Yves Moreau; Eva Kubala Havrdova; Maria Trojano; Sara Eichau; Serkan Ozakbas; Marco Onofrj; Pierre Grammond; Jens Kuhle; Ludwig Kappos; Patrizia Sola; et al. (Comput. Methods Programs Biomed., 2021)

Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression: [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180]. - Edward De Brouwer; Thijs Becker; Yves Moreau; Eva Kubala Havrdova; Maria Trojano; Sara Eichau; Serkan Ozakbas; Marco Onofrj; Pierre Grammond; Jens Kuhle; Ludwig Kappos; Patrizia Sola; et al. (Comput. Methods Programs Biomed., 2022)

Explainable time-to-progression predictions in multiple sclerosis. - Robbe D'hondt; Klest Dedja; Sofie Aerts; Bart Van Wijmeersch; Tomas Kalincik; Stephen Reddel; Eva Kubala Havrdova; Alessandra Lugaresi; Bianca Weinstock-Guttman; Saloua Mrabet; Patrice Lalive; Allan G. Kermode; et al. (Comput. Methods Programs Biomed., 2025)

Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data. - Ashkan Pirmani; Edward De Brouwer; Adam Arany; Martijn Oldenhof; Antoine Passemiers; Axel Faes; Tomas Kalincik; Serkan Ozakbas; Riadh Gouider; Barbara Willekens; Dana Horáková; Eva Kubala Havrdova; et al. (npj Digit. Medicine, 2025)