Creating a comprehensive dataset for critical care research using AI
CRITICAL: Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning
This study is working to gather and combine information from different hospitals to better understand and predict serious illnesses like acute kidney failure in ICU patients, with the hope that this will lead to better treatments and outcomes for you and others in similar situations.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Northwestern University at Chicago NIH-funded |
| Lab location | 1 site (Chicago, United States) |
| Project ID | NIH-10904909 on NIH RePORTER |
What this research studies
This research aims to develop a collaborative resource that combines data from multiple clinical sites to create a diverse and comprehensive dataset for intensive care unit (ICU) patients. By leveraging existing datasets and enhancing them with new data, the project seeks to improve the understanding and prediction of critical illnesses, particularly acute renal failure. Researchers will utilize advanced artificial intelligence techniques to analyze this data, ultimately aiming to accelerate the translation of findings into clinical practice. Patients may benefit from improved treatment protocols and outcomes as a result of this enhanced research capability.
Who could benefit from this research
Good fit: Ideal candidates for this research are critically ill patients admitted to ICUs, especially those with acute renal failure.
Not a fit: Patients with stable conditions who are not admitted to intensive care units may not receive any benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to better predictive models and treatment strategies for critically ill patients, particularly those suffering from acute renal failure.
How similar studies have performed: Previous research has shown success in utilizing large, diverse datasets for improving AI applications in healthcare, indicating that this approach has potential for significant advancements.
Where this research is happening
Chicago, United States
- Northwestern University at Chicago — Chicago, United States (Active)
Researchers
- Principal investigator: Luo, Yuan — Northwestern University at Chicago
- Study coordinator: Luo, Yuan
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.