Predicting disability worsening and inflammation in multiple sclerosis using advanced algorithms
Prediction of risk of disability worsening and inflammatory disease activity in MS utilizing multimodal prediction algorithms
This study is looking at how to better predict changes in disability and inflammation for people with multiple sclerosis, using data from medical records to help create personalized treatment plans that could improve their care.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Johns Hopkins University NIH-funded |
| Lab location | 1 site (Baltimore, United States) |
| Project ID | NIH-10932157 on NIH RePORTER |
What this research studies
This research focuses on understanding the unpredictable nature of multiple sclerosis (MS) by developing algorithms that can predict the risk of worsening disability and inflammatory activity in patients. By analyzing a wide range of data from electronic medical records, including demographics, clinical characteristics, and biological markers, the study aims to identify key factors that influence disease progression. The use of machine learning techniques allows for a more accurate assessment of individual patient risk, potentially leading to personalized treatment strategies. Patients may be monitored over time to validate these predictive models and improve clinical decision-making.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals diagnosed with multiple sclerosis, particularly those experiencing variability in their disease course.
Not a fit: Patients with stable, benign forms of multiple sclerosis who do not experience significant changes in their condition may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of disease progression in MS, allowing for timely and personalized treatment interventions.
How similar studies have performed: Previous research has shown promise in using big data and machine learning approaches to predict outcomes in various medical conditions, suggesting that this approach could be effective in MS as well.
Where this research is happening
Baltimore, United States
- Johns Hopkins University — Baltimore, United States (Active)
Researchers
- Principal investigator: Sotirchos, Elias S — Johns Hopkins University
- Study coordinator: Sotirchos, Elias S
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.