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 (POMs) and demographic data. In our work we extend on this to focus on the modeling side, using continuous time models on POMs and demographic data to predict progression. We evaluate four continuous time models using a publicly available multiple sclerosis dataset. We find that continuous models are often able to outperform discrete time models. 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.

翻译：多发性硬化症是一种影响大脑和脊髓的疾病，可能导致严重残疾，且尚无已知治愈方法。此前大多数关于多发性硬化症的机器学习研究主要集中于使用磁共振成像扫描或实验室检测；这些检查方式成本高昂且可能不可靠。近期一篇论文表明，利用绩效结果指标（POMs）和人口统计数据可有效预测疾病进展。本研究在此基础上聚焦于建模层面，通过将连续时间模型应用于POMs和人口统计数据来预测疾病进展。我们基于公开的多发性硬化症数据集对四种连续时间模型进行了评估，发现连续模型通常优于离散时间模型。此外，我们通过广泛的消融实验探究性能提升的来源，结果表明标准化现有特征带来的性能提升幅度大于对缺失特征进行插值处理。