Developing a model to improve predictions from clinical data
Official title: Clinical foundation model for structured clinical data
Funded at University of Texas Hlth Sci Ctr Houston · NIH-11103340 · Data updated 2026-11-12
This study is working on a new way to use health data from different sources to make better predictions about medical events, which could help improve care and lower costs for patients like you.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Texas Hlth Sci Ctr Houston NIH-funded |
| Lab location | 1 site (Houston, United States) |
| Project start | September 1, 2023 |
| Project end | May 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $351,000 |
| NIH project number | 5R01LM014249-03 |
| Project ID | NIH-11103340 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients whose clinical data can be utilized for predictive modeling, particularly those with chronic conditions like COVID-19 or cardiac failure.
✕Less likely to help
Patients with very localized or unique clinical conditions that do not align with the broader predictive models may not benefit from this research.
What this research studies
This research focuses on creating a clinical foundation model (CFM) that utilizes structured clinical data from various sources, such as electronic health records (EHR) and claims data. By addressing the limitations of current predictive models, which often rely on localized data, the project aims to enhance the accuracy and generalizability of predictions for various clinical events. The approach includes developing a flexible framework for data integration, optimizing model architecture, and implementing innovative learning strategies. Patients' structured clinical data will be leveraged to improve healthcare efficiency and reduce costs.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of clinical outcomes, ultimately improving patient care and reducing healthcare costs.
How similar studies have gone: Other research has shown success in using large-scale clinical data to improve predictive modeling, indicating that this approach has potential for significant advancements.
Where this research is happening
-
University of Texas Hlth Sci Ctr Houston
Houston, United States Active
Researchers
- Principal investigator: Bekhet, Laila Rasmy Gindy — University of Texas Hlth Sci Ctr Houston
- Study coordinator: Bekhet, Laila Rasmy Gindy
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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.