Personalized AI to predict sudden kidney injury
Personalized Machine Learning for Acute Kidney Injury Prediction and Prognosis
This project uses personalized machine learning on hospital records to predict which hospitalized patients are likely to develop acute kidney injury so clinicians can act sooner.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Florida NIH-funded |
| Lab location | 1 site (Gainesville, United States) |
| Project ID | NIH-11345244 on NIH RePORTER |
What this research studies
If I'm a hospitalized patient, researchers will use my electronic health record—including labs, medications, and vital signs—to build a computer model tailored to patients like me that estimates short-term risk of acute kidney injury. They will compare these personalized models to traditional one-size-fits-all models to find hidden patient subgroups and improve prediction accuracy. The team will train and test models using hospital EHR data and prior examples where global models miss high-risk patients. The aim is to give doctors earlier, more specific warnings so they can target prevention and reduce kidney damage.
Who could benefit from this research
Good fit: Hospitalized adults with risk factors for acute kidney injury—especially critically ill patients—with available electronic health records are the most likely candidates.
Not a fit: People who are not hospitalized, who lack comprehensive hospital EHR data, or whose kidney problems are chronic and unrelated to in-hospital acute events may not benefit from this work.
Why it matters
Potential benefit: If successful, this could provide earlier, tailored warnings that help prevent or reduce the severity of acute kidney injury and its long-term complications.
How similar studies have performed: Other machine-learning models have improved AKI prediction in hospitals, but personalized, subgroup-focused approaches are newer and less tested in routine care.
Where this research is happening
Gainesville, United States
- University of Florida — Gainesville, United States (Active)
Researchers
- Principal investigator: Liu, Mei — University of Florida
- Study coordinator: Liu, Mei
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.