Using machine learning to predict kidney disease risk in children born extremely preterm
Machine Learning Risk Prediction of Kidney Disease After Extremely Preterm Birth
This study is working on creating helpful tools to spot kidney disease risks in kids who were born very early, so doctors can better keep an eye on their kidney health.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of North Carolina Chapel Hill NIH-funded |
| Lab location | 1 site (Chapel Hill, United States) |
| Project ID | NIH-11081722 on NIH RePORTER |
What this research studies
This research aims to develop tools that help identify the risk of kidney disease in children who were born extremely preterm. By utilizing machine learning techniques, the project will analyze large datasets to improve the recognition of these risks. Dr. Keia Sanderson, the principal investigator, will work with a team of experts to enhance her skills in research leadership and qualitative methods, ensuring that the findings can be effectively applied in clinical settings. The goal is to create a framework that can be used by healthcare providers to monitor and manage kidney health in this vulnerable population.
Who could benefit from this research
Good fit: Ideal candidates for this research are children under 11 years old who were born extremely preterm.
Not a fit: Patients who were not born extremely preterm or who are over 11 years old may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to earlier detection and better management of kidney disease in children born extremely preterm.
How similar studies have performed: Similar research has shown promise in using machine learning for predicting health outcomes in pediatric populations, indicating a potential for success in this approach.
Where this research is happening
Chapel Hill, United States
- Univ of North Carolina Chapel Hill — Chapel Hill, United States (Active)
Researchers
- Principal investigator: Sanderson, Keia — Univ of North Carolina Chapel Hill
- Study coordinator: Sanderson, Keia
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.