Current care in multiple sclerosis (MS) primarily relies on infrequently obtained data such as magnetic resonance imaging (MRI), clinical laboratory tests or clinical history, resulting in subtle changes that may occur between visits being missed. Mobile technology enables continual collection of data and can pave the path for predicting complex aspects of MS such as symptoms and disease courses. To this end, we conducted a first-of-its-kind observational study called MS Mosaic. First, we developed and publicly launched a mobile app for collecting longitudinal data from MS subjects in the United States. Second, we ran the study across 3 years in order to capture complex patterns for this slow progressing disease. Finally, we retrospectively developed three classical ML methods and two deep learning models to accurately and continually predict the incidence of five high-severity symptoms (fatigue, sensory disturbance, walking instability, depression or anxiety and cramps) three months in advance.

Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for disease identification. However, studies have shown that these modalities are not consistent with disease activity such as symptoms or disease progression. Furthermore, the cost of collecting data from these modalities is high, leading to scarce evaluations. In this work, we used multi-dimensional, affordable, physical and smartphone-based performance outcome measures (POM) in conjunction with demographic data to predict multiple sclerosis disease progression. We performed a rigorous benchmarking exercise on two datasets and present results across 13 clinically actionable prediction endpoints and 6 machine learning models. To the best of our knowledge, * These authors contributed equally † Data used in the preparation of this article were obtained from the Multiple Sclerosis Outcome Assessments Consortium (MSOAC). As such, the investigators within MSOAC contributed to the design and implementation of the MSOAC Placebo database and/or provided placebo data, but did not participate in the analysis of the data or the writing of this report.