Using AI to improve risk assessment for patients with acute gastrointestinal bleeding
Official title: Deep Learning Approaches to Risk Stratification in Acute Gastrointestinal Bleeding
Funded at Yale University · NIH-10918333 · Data updated 2026-11-12
This study is working on a new tool that uses advanced technology to help doctors better understand the risk of patients having sudden stomach bleeding, so they can make smarter decisions about who needs to be hospitalized and when.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | September 1, 2021 |
| Project end | August 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $193,860 |
| NIH project number | 5K23DK125718-04 |
| Project ID | NIH-10918333 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients presenting with symptoms of acute gastrointestinal bleeding, such as vomiting blood or passing black stools.
✕Less likely to help
Patients with gastrointestinal bleeding who do not present with acute symptoms or those with chronic conditions may not benefit from this research.
What this research studies
This research focuses on developing advanced machine learning models to better assess the risk of patients experiencing acute gastrointestinal bleeding. By utilizing data from electronic health records, the study aims to create a more accurate risk stratification tool based on presenting symptoms rather than the location of the bleeding. This approach seeks to reduce unnecessary hospitalizations and optimize resource use in emergency departments. The use of deep learning techniques allows for continuous improvement of the risk assessment models as new data becomes available.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and timely assessments for patients with acute gastrointestinal bleeding, potentially reducing unnecessary hospital stays and improving patient outcomes.
How similar studies have gone: Other research has shown promising results using machine learning for risk assessment in various medical conditions, indicating that this approach has the potential for success.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Shung, Dennis — Yale University
- Study coordinator: Shung, Dennis
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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.