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)

Deciphering the Morphology of Motor Evoked Potentials. - Jan Yperman; Thijs Becker; Dirk Valkenborg; Niels Hellings; Melissa Cambron; Dominique Dive; Guy Laureys; Veronica Popescu; Bart Van Wijmeersch; Liesbet M. Peeters (Frontiers Neuroinformatics, 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)

Strategic Oversight Across Real-World Health Data Initiatives in a Complex Health Data Space: A Call for Collective Responsibility. - Lotte Geys; Liesbet M. Peeters (BIOSTEC (2), 2024)

Measuring Approximate Functional Dependencies: A Comparative Study. - Marcel Parciak; Sebastiaan Weytjens; Niel Hens; Frank Neven; Liesbet M. Peeters; Stijn Vansummeren (ICDE, 2024)

Unlocking the Power of Real-World Data: A Framework for Sustainable Healthcare. - Ashkan Pirmani; Yves Moreau; Liesbet M. Peeters (MIE, 2024)

Schema Matching with Large Language Models: an Experimental Study. - Marcel Parciak; Brecht Vandevoort; Frank Neven; Liesbet M. Peeters; Stijn Vansummeren (VLDB Workshops, 2024)

Federated Block-Term Tensor Regression for decentralised data analysis in healthcare. - Axel Faes; Ashkan Pirmani; Yves Moreau; Liesbet M. Peeters (CoRR, 2024)

LLM-Matcher: A Name-Based Schema Matching Tool using Large Language Models. - Marcel Parciak; Brecht Vandevoort; Frank Neven; Liesbet M. Peeters; Stijn Vansummeren (SIGMOD Conference Companion, 2025)

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)

An Algorithmic Pipeline for GDPR-Compliant Healthcare Data Anonymisation: Moving Toward Standardisation. - Hamza Khan; Lore Menten; Liesbet M. Peeters (CoRR, 2025)