Improving predictions of disease cure using machine learning
Official title: Using Machine Learning to Improve the Predictive Accuracy of Disease Cure
Funded at University of Texas Arlington · NIH-10654253 · Data updated 2026-11-12
This study is working on a smart tool that uses computer technology to help doctors figure out which patients with early-stage diseases might get better on their own, so they can avoid harsh treatments, while also identifying those who need quick care to stay healthy.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Texas Arlington NIH-funded |
| Lab location | 1 site (Arlington, United States) |
| Project start | June 1, 2023 |
| Project end | May 31, 2026 |
| Funding year | 2023 |
| Award (this year) | $452,082 |
| NIH project number | 1R15GM150091-01 |
| Project ID | NIH-10654253 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 who do not have sufficient pre-treatment data may not benefit from this research.
What this research studies
This research aims to develop a predictive model that utilizes machine learning techniques to accurately identify patients who are likely to be cured of early-stage diseases. By analyzing pre-treatment characteristics and survival data, the model will help distinguish between patients who can safely avoid aggressive treatments and those who need timely intervention to prevent disease progression. The approach addresses limitations of existing predictive models, enhancing their robustness and applicability in clinical settings.
Why it matters
Potential benefit: If successful, this research could lead to more personalized treatment plans, reducing unnecessary treatments for cured patients and improving outcomes for those who are not yet cured.
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.