Big data in multiple sclerosis

Area of Science:

• Neuroimmunology
• Data Science in Medicine
• Computational Biology

Background:

• Multiple sclerosis (MS) is a chronic autoimmune disease affecting the central nervous system.
• Understanding MS progression and optimizing treatment requires advanced analytical approaches.

Purpose of the Study:

• To review key advancements in multiple sclerosis (MS) research driven by big data analytics.
• To highlight the impact of real-world evidence (RWE) on MS treatment and prognosis.

Main Methods:

• Systematic review of recent literature on big data applications in MS.
• Analysis of diverse data sources including clinical records, MRI, genomics, and biomarkers.
• Utilization of advanced analytical techniques and artificial intelligence (AI).

Main Results:

• Real-world evidence (RWE) from MS big data has improved treatment strategies and personalized medicine.
• Big data redefined disease progression, refined prognostic models, and informed treatment decisions during pregnancy.
• Multimodal data frameworks enhance diagnostic performance and risk stratification in MS.
• Progression independent of relapse activity is a key driver of disability.

Conclusions:

• Big data approaches are transforming MS research and clinical practice.
• RWE guides therapeutic decision-making and refines disease progression models.
• Advanced analytics are crucial for developing precise prognostic tools in MS.