Using machine learning to predict kidney disease risk in children born extremely preterm

Who could benefit from this research

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.

Why it matters

Where this research is happening

• 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

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About this research

1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
3. For full project details, budget, and progress reports, visit the official NIH RePORTER page.