Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression
This work addresses the problem of predicting disease progression for multiple sclerosis patients using more accessible data, but it is incremental as it builds on prior findings to focus on modeling improvements.
The paper tackled predicting multiple sclerosis progression by benchmarking continuous time models against discrete ones, finding that the best continuous model often outperforms the best discrete model, with performance gains primarily from standardizing features rather than interpolating missing ones.
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.