Using machine learning to model the prognosis of multiple sclerosis patients

Multiple sclerosis is a complex disease with a highly heterogeneous disease course. In this project, we will develop machine learning methods to study this disease course using a combination of demographical, clinical, genomic, and radiomic data. This study is done on the patient level (individualized prognosis models), on the group level (patient stratification models), and on the population level (identification of prognostic biomarkers). With this study, we address pressing needs from both the clinicians, who want to start the right treatment for the right patient as soon as possible, as well as from the pharmacological industry, which wants to develop new treatments as cost-efficiently as possible. In the process, we will contribute to the machine learning literature on genetic data analysis, multi-target learning (i.e. combining outcomes of different data types), recurrent event survival analysis, and clustering (i.e. dynamic over time). Specifically, the models developed in this thesis will need to deal with sporadic and irregularly sampled time series, missing values (even in the outcome space), and confounding factors. To maximize the probability of integration in clinical practice, we additionally ensure that the models are explainable, reliable, and trustworthy.