Predictive models of multiple sclerosis-related cognitive performance using routine clinical practice predictors.

Background: The application of machine learning (ML) to predict cognitive evolution is exceptionally scarce. Computer-based self-administered cognitive tests provide the opportunity to set up large longitudinal datasets to aid in developing ML prediction models of risk for Multiple Sclerosis-related cognitive decline.

Objective: to analyze to what extent clinically feasible models can be built with standard clinical practice features and subsequently used for reliable prediction of cognitive evolution.

Risk acceptance in multiple sclerosis patients on natalizumab treatment.

Objective: We aimed to investigate the ability of natalizumab (NTZ)-treated patients to assume treatment-associated risks and the factors involved in such risk acceptance.

Methods: From a total of 185 patients, 114 patients on NTZ as of July 2011 carried out a comprehensive survey. We obtained disease severity perception scores, personality traits' scores, and risk-acceptance scores (RAS) so that higher RAS indicated higher risk acceptance.