Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity.

ABSTRACT:

Background

Lack of easy-to-interpret disease activity prediction methods in early MS can lead to worse patient prognosis.

Objectives

Using machine learning (multiple kernel learning - MKL) models, we assessed the prognostic value of various clinical and MRI measures for disease activity.

Methods

Early MS patients (n = 148) with at least two associated clinical and MRI visits were investigated. T2-weighted MRIs were cropped to contain mainly the lateral ventricles (LV). High disease activity was defined as surpassing NEDA-3 Criteria more than once per year. Clinical demographic, MRI-extracted image-derived phenotypes (IDP), and MRI data were used as inputs for separate kernels to predict future disease activity with MKL. Model performance was compared using boot

SUBMITTER: Chien C 

PROVIDER: S-EPMC9260586 | biostudies-literature | 2022 Jul-Sep

REPOSITORIES: biostudies-literature