Longitudinal modeling of MS patient trajectories improves predictions of disability progression

TL;DR

This paper demonstrates that modeling longitudinal MS patient data with advanced machine learning improves disability progression predictions over static models, leveraging real-world clinical data for more accurate insights.

Contribution

It introduces a novel approach using recurrent neural networks and tensor factorization to effectively extract information from sporadically sampled longitudinal data in MS.

Findings

Achieved ROC-AUC of 0.86 in predicting disability progression.

33% reduction in ranking pair error compared to static feature models.

Utilized the most comprehensive patient history for MS progression prediction.

Abstract

Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work, we address the task of optimally extracting information from longitudinal patient data in the real-world setting with a special focus on the sporadic sampling problem. Using the MSBase registry, we show that with machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization, we can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.86, which represents a 33% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction.