Using machine learning to improve urinary tract infection care in children
Official title: Machine-learning prediction model for personalized urinary tract infection care in children
Funded at Boston Children's Hospital · NIH-10983858 · Data updated 2026-11-12
This study is working on a smart tool to help doctors figure out which kids with urinary tract infections might be at risk for kidney problems, so they can get the right care at the right time.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Boston Children's Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | July 1, 2024 |
| Project end | June 30, 2029 |
| Funding year | 2024 |
| Award (this year) | $153,050 |
| NIH project number | 1K08HS029526-01A1 |
| Project ID | NIH-10983858 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are children diagnosed with febrile urinary tract infections who may require further diagnostic evaluation.
✕Less likely to help
Patients with urinary tract infections who do not present with fever or those who have already sustained renal injury may not benefit from this research.
What this research studies
This research aims to enhance the management of febrile urinary tract infections (fUTI) in children by developing a machine learning model that predicts which children are at risk for renal injury. The approach involves creating a clinical decision support algorithm that helps healthcare providers identify unsafe anatomical conditions before they lead to injury. By analyzing data, the model will assist in determining the optimal timing for diagnostic procedures, ensuring timely and effective care for affected children.
Why it matters
Potential benefit: If successful, this research could significantly reduce the risk of kidney damage in children suffering from urinary tract infections.
How similar studies have gone: Previous research has shown promise in using machine learning models for predicting health outcomes, suggesting that this approach could be effective in this context as well.
Where this research is happening
-
Boston Children's Hospital
Boston, United States Active
Researchers
- Principal investigator: Wang, Hsin-Hsiao Scott — Boston Children's Hospital
- Study coordinator: Wang, Hsin-Hsiao Scott
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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.