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Related Experiment Video

Updated: Jul 25, 2025

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
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Predictive models of multiple sclerosis-related cognitive performance using routine clinical practice predictors.

Andrés Labiano-Fontcuberta1, Lucienne Costa-Frossard2, Susana Sainz de la Maza2

  • 1Department of Neurology, University Hospital12 de Octubre, Avenida de Córdoba 41, Community of Madrid 28026, Spain.

Multiple Sclerosis and Related Disorders
|June 25, 2023
PubMed
Summary

Machine learning models can predict cognitive decline in Multiple Sclerosis (MS) patients. Trees-based models accurately forecast information processing speed changes using clinical data and treatment timing.

Keywords:
Cognitive dysfunctionHigh efficacy disease-modifying therapiesInformation processing speedMachine learningMultiple sclerosisProcessing speed test

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Machine learning (ML) applications for predicting cognitive evolution are limited.
  • Computer-based cognitive tests generate large datasets for ML model development.
  • Predicting cognitive decline in Multiple Sclerosis (MS) is crucial.

Purpose of the Study:

  • To assess the feasibility of building predictive models using standard clinical features.
  • To evaluate the reliability of these models in predicting cognitive evolution.
  • To identify key predictors for cognitive changes in MS.

Main Methods:

  • A prospective longitudinal study of 1184 people with MS.
  • Processing Speed (PS) was measured using the iPad®-based Processing Speed Test (PST) at 12 months.
  • Six ML classification models were trained and tested using routine clinical data.

Main Results:

  • 15.8% of people with MS showed a 10% reduction in PST score (PST worsening).
  • Trees-based models (Random Forest, eXtreme Gradient Boosting) showed high performance (AUC 0.90, 0.89).
  • Timing of high-efficacy disease-modifying therapies (heDMTs) was a key predictor.

Conclusions:

  • Trees-based ML models show promise for predicting individual information processing speed decline in MS.
  • These models could potentially be integrated into clinical practice.
  • Predictive analytics can aid in managing MS-related cognitive changes.