Improving Care for Acute Kidney Injury with Smart Technology
Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury
This project uses smart computer programs to find acute kidney injury sooner in hospitalized patients and help doctors choose the best treatments for them.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Chicago NIH-funded |
| Lab location | 1 site (Chicago, United States) |
| Project ID | NIH-11141140 on NIH RePORTER |
What this research studies
Acute kidney injury (AKI) is a serious condition that affects many hospitalized patients, often leading to higher risks and costs. Current methods for finding AKI can be slow, meaning treatments might start too late to be most effective. This project aims to create a more advanced computer program that uses information from patient health records, including doctors' notes, to detect AKI much earlier. By improving on existing tools that sometimes give false alarms, this new approach seeks to provide more accurate and timely alerts to healthcare providers.
Who could benefit from this research
Good fit: Hospitalized patients at risk for or experiencing acute kidney injury would be the focus of this research.
Not a fit: Patients not hospitalized or those without acute kidney injury would not directly benefit from this specific early detection tool.
Why it matters
Potential benefit: If successful, this work could lead to earlier and more effective treatments for acute kidney injury, potentially reducing complications and improving recovery for hospitalized patients.
How similar studies have performed: While prior machine learning tools have shown promise in early AKI detection, this project aims to significantly improve accuracy by incorporating more detailed patient information.
Where this research is happening
Chicago, United States
- University of Chicago — Chicago, United States (Active)
Researchers
- Principal investigator: Koyner, Jay L — University of Chicago
- Study coordinator: Koyner, Jay L
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.