Using machine learning to predict individual risk of acute kidney injury
Official title: Identifying Personalized Risk of Acute Kidney Injury with Machine Learning
Funded at University of Florida · NIH-10992250 · Data updated 2026-11-12
This study is looking at how to better predict the risk of kidney problems in patients in the hospital by using smart computer techniques, so doctors can give more personalized care based on each person's health history.
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 | December 1, 2023 |
| Project end | June 30, 2025 |
| Funding year | 2021 |
| Award (this year) | $271,307 |
| NIH project number | 7R01DK116986-04 |
| Project ID | NIH-10992250 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include hospitalized patients who are at risk of developing acute kidney injury due to various underlying health conditions.
✕Less likely to help
Patients who are not hospitalized or do not have risk factors for acute kidney injury may not benefit from this research.
What this research studies
This research focuses on understanding and predicting the risk of acute kidney injury (AKI) in hospitalized patients using advanced machine learning techniques. By analyzing complex electronic health record data, the study aims to develop personalized risk models that take into account the unique health profiles of individual patients rather than relying on generalized predictions. This approach seeks to improve the accuracy of AKI risk assessments, enabling healthcare providers to tailor interventions and treatments more effectively. The research will involve collecting and analyzing data from various patient cohorts to refine these predictive models.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of acute kidney injury, allowing for timely and personalized treatment interventions that could save lives.
How similar studies have gone: Previous research has shown success in using machine learning for disease risk predictions, indicating that this approach has potential for effective application in predicting acute kidney injury.
Where this research is happening
-
University of Florida
Gainesville, United States Active
Researchers
- Principal investigator: Liu, Mei — University of Florida
- Study coordinator: Liu, Mei
Related research
Other NIH-funded projects on the same topics.
-
SEAK Alliance: Improving Care for AKI Patients in the Southeast
A national, patient-centered effort to improve post-hospital care for people who had acute kidney injury in the Southeast.
-
AKI-related delirium after heart surgery
This grant funds research to understand why some people develop delirium after heart surgery when they also have acute kidney injury.
-
Better ways to identify types of sudden kidney injury in U.S. Veterans
Researchers are looking for genetic and health patterns that define different types of sudden severe kidney injury in Veterans to help guide future care.
-
Neuroimmune control of kidney injury using noninvasive ultrasound
A new approach uses targeted ultrasound to gently stimulate nerves and immune signals to help the kidney recover after injury.
-
Structure-based approach to prevent kidney injury from polymyxin use
Aims to block how polymyxin damages the kidney so it can be used more safely to treat hard-to-kill infections.
-
Using smart computer predictions to prevent kidney injury after heart surgery
A machine-learning approach uses routinely collected hospital data to predict who might develop acute kidney injury after heart surgery, so clinicians can act early.
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.