  Multiple sclerosis is a disease that affects the brain and spinal cord, it
can lead to severe disability and has no known cure. The majority of prior work
in machine learning for multiple sclerosis has been centered around using
Magnetic Resonance Imaging scans or laboratory tests; these modalities are both
expensive to acquire and can be unreliable. In a recent paper it was shown that
disease progression can be predicted effectively using performance outcome
measures and demographic data. In our work we build on this to investigate the
modeling side, using continuous time models to predict progression. We
benchmark four continuous time models using a publicly available multiple
sclerosis dataset. We find that the best continuous model is often able to
outperform the best benchmarked discrete time model. We also carry out an
extensive ablation to discover the sources of performance gains, we find that
standardizing existing features leads to a larger performance increase than
interpolating missing features.

Machine Learning