Using machine learning to predict disease outcomes and improve treatment methods
Official title: Machine Learning-Based Predictive Models for Disease Cure and Computationally Efficient Methods in High-Dimensional Settings
Funded at University of Texas Arlington · NIH-11019400 · Data updated 2026-11-12
This study is working on smart computer programs that can help doctors figure out if patients with early-stage diseases are cured or still need treatment, so they can avoid giving unnecessary intense treatments to those who are already better.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Texas Arlington NIH-funded |
| Lab location | 1 site (Arlington, United States) |
| Project start | May 1, 2025 |
| Project end | April 30, 2030 |
| Funding year | 2025 |
| Award (this year) | $278,104 |
| NIH project number | 1R35GM156859-01 |
| Project ID | NIH-11019400 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients diagnosed with early-stage diseases who are undergoing treatment and have available pre-treatment data.
✕Less likely to help
Patients with advanced-stage diseases or those without sufficient pre-treatment data may not benefit from this research.
What this research studies
This research focuses on developing advanced machine learning models to accurately predict whether patients with early-stage diseases are cured or uncured based on their pre-treatment characteristics. By analyzing high-dimensional patient data, the project aims to enhance early detection and intervention strategies, thereby reducing unnecessary high-intensity treatments for those who are already cured. The methodology involves creating predictive models that are more robust and clinically applicable than existing approaches, addressing current limitations in predictive accuracy and usability in clinical settings.
Why it matters
Potential benefit: If successful, this research could lead to more personalized treatment plans, minimizing unnecessary treatments for cured patients and improving outcomes for those who are uncured.
How similar studies have gone: Previous research has shown promise in using machine learning for predictive modeling in healthcare, indicating potential success for this novel approach.
Where this research is happening
-
University of Texas Arlington
Arlington, United States Active
Researchers
- Principal investigator: Pal, Suvra — University of Texas Arlington
- Study coordinator: Pal, Suvra
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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.