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Towards Multiple Sclerosis Personalised Interventions Based on Real-World Predictive Analytics.

Konstantinos Aggelopoulos1, Georgios Petridis2, Alexandra Anagnostopoulou2

  • 1Interdisciplinary Postgraduate Program in Advanced Computer and Communication Systems, Aristotle University of Thessaloniki, Greece.

Studies in Health Technology and Informatics
|May 17, 2025
PubMed
Summary

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This summary is machine-generated.

Machine learning accurately predicts treatment response in Multiple Sclerosis patients using wearable device data. Early predictions allow for personalized interventions, improving patient quality of life.

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Multiple Sclerosis (MS) is a chronic neurological disease impacting cognitive function.
  • Personalized treatment strategies are crucial for managing MS progression and improving patient outcomes.
  • Wearable devices offer a promising avenue for collecting real-world data in MS patients.

Purpose of the Study:

  • To investigate the efficacy of machine learning (ML) models in predicting intervention response in patients with Multiple Sclerosis (PwMS).
  • To utilize real-world data from wearable devices for early identification of treatment efficacy.
  • To enhance personalized treatment strategies for cognitive decline in PwMS.

Main Methods:

  • Analysis of data from 27 PwMS monitored via wearable devices over two months.
Keywords:
computerized interventionmultiple sclerosispersonalized medicinereal-world datatreatment responsewearables

Related Experiment Videos

  • Application of various state-of-the-art ML models, including Support Vector Machines (SVM).
  • Utilized feature selection techniques such as Mutual Information and Recursive Feature Elimination.

    • Main Results:

    • A Support Vector Machine model demonstrated high accuracy in predicting patient response to a computerized cognitive intervention.
    • Early prediction of intervention efficacy was achieved within the first 2-3 weeks.
    • Feature selection methods significantly aided the predictive performance of the ML models.

    Conclusions:

    • ML techniques, particularly SVM, can accurately predict intervention response in PwMS using wearable sensor data.
    • Early prediction facilitates timely therapeutic adjustments, enabling personalized treatment plans.
    • This approach has the potential to significantly improve the quality of life for patients with Multiple Sclerosis.