Using machine learning to predict and understand acute kidney injury
Official title: Personalized Machine Learning for Acute Kidney Injury Prediction and Prognosis
Funded at University of Florida · NIH-11073027 · Data updated 2026-11-12
This study is working on using advanced computer techniques to better predict and understand acute kidney injury (AKI) for each patient, so doctors can spot those at higher risk early and provide the right care to prevent serious problems.
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 start | April 15, 2024 |
| Project end | March 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $261,465 |
| NIH project number | 5R01DK137881-02 |
| Project ID | NIH-11073027 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include hospitalized patients, particularly those who are critically ill and at high risk for acute kidney injury.
✕Less likely to help
Patients with stable kidney function and those not hospitalized may not receive benefit from this research.
What this research studies
This research focuses on improving the prediction and prognosis of acute kidney injury (AKI) using personalized machine learning techniques. By analyzing complex data from electronic health records, the study aims to create tailored risk assessment models that account for the unique characteristics of individual patients rather than relying on a one-size-fits-all approach. This personalized method seeks to identify high-risk patients early, allowing for timely interventions to prevent severe outcomes associated with AKI.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of acute kidney injury, enabling earlier and more effective treatments for patients.
How similar studies have gone: Previous research has shown promise in using machine learning for disease prediction, indicating that this approach could be effective for acute kidney injury as well.
Where this research is happening
-
University of Florida
Gainesville, United States Active
Researchers
- Principal investigator: Liu, Mei — University of Florida
- Study coordinator: Liu, Mei
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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.