In this project, we aim to create computational models for predicting the course of the disease and stratifying for treatment responses to support clinicians. Building on a data corpus collected with our partners at the University Hospital Zurich in a previous PHRT project as well as ongoing studies, we will adapt and develop novel machine learning methods to exploit the variety of data modalities focusing on disease state characterization and disease progression modeling. This includes natural language processing (NLP) for features extraction from clinical data, automatic feature detection in magnetic resonance images (MRI), models for longer-term temporal data, and integrative models that combine information from these three different data modalities and sources. Together, our models will allow generating reports for longitudinal comparisons of MS patients to support patient care.