We develop and apply a semi-supervised generative deep learning framework to model Systemic Sclerosis patient trajectories in the EUSTAR database (~14,000 patients across 67,000 medical visits) and identify clinically meaningful subtypes beyond the existing limited/diffuse classification. We aim to discover new patient clusters by modeling multi-organ involvement over time, which is key for better stratification and prognosis. (read more here: link )