Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

ABSTRACT:

Background

Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.

Methods

We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre.

Results

We found algorit

SUBMITTER: Andorra M 

PROVIDER: S-EPMC10896787 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature