A Binning Approach for Predicting Long-Term Prognosis in Multiple Sclerosis. - Robbe D'hondt; Sinead Moylett; An Goris; Celine Vens (AIME, 2023)

Using Machine Learning to Predict the Number of Latent Skills in Online Learning Environments. - Changsheng Chen; Robbe D'hondt; Celine Vens; Wim van den Noortgate (ALL@AIED, 2024)

ITEC at BEA 2024 Shared Task: Predicting Difficulty and Response Time of Medical Exam Questions with Statistical, Machine Learning, and Language Models. - Anaïs Tack; Siem Buseyne; Changsheng Chen; Robbe D'hondt; Michiel De Vrindt; Alireza Gharahighehi; Sameh Metwaly; Felipe Kenji Nakano; Ann-Sophie Noreillie (BEA, 2024)

Multi-Event Survival-Informed Clustering for Time-to-Worsening in Multiple Sclerosis. - Robbe D'hondt; Jasper de Boer; Celine Vens (AIME (1), 2025)

Integrating Expert Knowledge in Matrix Factorization. - Changsheng Chen; Robbe D'hondt; Alireza Gharahighehi; Celine Vens; Wim van den Noortgate (LAK Workshops, 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)