A temporal model for multiple sclerosis course evolution

TL;DR

This paper introduces a machine learning pipeline to predict multiple sclerosis progression using patient-reported outcome measures, aiming to improve understanding of disease evolution.

Contribution

It presents a novel application of regularized machine learning methods for predicting MS progression from patient-reported data.

Findings

Effective prediction of disease progression from patient reports

Validation on clinical research data shows promising results

Provides a new tool for disease monitoring and management

Abstract

Multiple Sclerosis is a degenerative condition of the central nervous system that affects nearly 2.5 million of individuals in terms of their physical, cognitive, psychological and social capabilities. Researchers are currently investigating on the use of patient reported outcome measures for the assessment of impact and evolution of the disease on the life of the patients. To date, a clear understanding on the use of such measures to predict the evolution of the disease is still lacking. In this work we resort to regularized machine learning methods for binary classification and multiple output regression. We propose a pipeline that can be used to predict the disease progression from patient reported measures. The obtained model is tested on a data set collected from an ongoing clinical research project.