Predicting disease activity in patients with multiple sclerosis: An explainable machine-learning approach in the Mavenclad trials.

ABSTRACT: Multiple sclerosis (MS) is among the most common autoimmune disabling neurological conditions of young adults and affects more than 2.3 million people worldwide. Predicting future disease activity in patients with MS based on their pathophysiology and current treatment is pivotal to orientate future treatment. In this respect, we used machine learning to predict disease activity status in patients with MS and identify the most predictive covariates of this activity. The analysis is conducted on a pooled population of 1935 patients enrolled in three cladribine tablets clinical trials with different outcomes: relapsing-remitting MS (from CLARITY and CLARITY-Extension trials) and patients experiencing a first demyelinating event (from the ORACLE-MS trial). We applied gradient-boosting (from X

SUBMITTER: Basu S 

PROVIDER: S-EPMC9286719 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature