Integrating Clinical Data and Patient-Reported Outcomes for Analyzing Gender Differences and Progression in Multiple...

Area of Science:

• Neuroscience
• Medical Informatics

Background:

• Multiple sclerosis (MS) presents complex challenges in prognosis and management due to its variable nature.
• Understanding disease progression and gender-specific patterns is crucial for effective patient care.

Purpose of the Study:

• To apply machine learning (ML) for enhanced understanding of MS disease progression.
• To identify and analyze gender-based differences in MS clinical outcomes and quality of life.
• To explore the utility of integrated clinical and patient-reported data in MS management.

Main Methods:

• Prospective cohort study of 250 MS patients over 18 months.
• Utilized Decision Trees, Random Forest, and Support Vector Machine algorithms for patient classification and disability prediction (Expanded Disability Status Scale - EDSS).
• Employed propensity score matching to investigate gender differences in clinical outcomes and quality of life.

Main Results:

• ML models demonstrated high accuracy in classifying MS types and predicting disability levels.
• Significant gender differences were identified in MS disease progression and treatment response.
• Integrated data analysis via ML improved diagnostic accuracy and supported clinical decision-making.

Conclusions:

• Machine learning offers a powerful tool for improving the accuracy of MS diagnosis and prognosis.
• The study highlights the necessity of a gender-specific approach in managing multiple sclerosis.
• Personalized medicine, informed by integrated data and ML, holds transformative potential for MS patient care.