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.
Multiple Sclerosis (MS) is highly heterogeneous in clinical and imaging presentation and key pathophysiological processes of inflammation and neurodegeneration. Hence, establishing an accurate clinical picture of disease activity and progression in individual patients is a core challenge requiring knowledge and integration of the key elements driving the disease and its temporal evolution. Essential elements are the spectrum of neurological manifestation, imaging markers of disease burden, patient age, disease duration, initial symptoms, previous treatment response, and to some extent, cerebrospinal fluid or blood biomarkers. The highly complex interplay of these many different features requires a high level of experience by the clinician.
