A temporal model for multiple sclerosis course evolution
This work addresses the need for better prediction tools for Multiple Sclerosis patients, but it appears incremental as it applies existing machine learning methods to this domain.
The researchers tackled the problem of predicting Multiple Sclerosis disease progression using patient-reported outcome measures, achieving a predictive model tested on clinical data.
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.