Creating advanced models to detect sepsis early
Official title: Develop Multi-modal Foundation Models for Sepsis Early Detection
Funded at University of California, San Diego · NIH-11021373 · Data updated 2026-11-12
This study is working on smart computer programs that can look at different types of patient information, like medical images and health records, to spot early signs of sepsis, helping doctors catch it sooner and provide better care.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Diego NIH-funded |
| Lab location | 1 site (La Jolla, United States) |
| Project start | February 1, 2025 |
| Project end | January 31, 2030 |
| Funding year | 2025 |
| Award (this year) | $312,974 |
| NIH project number | 1R35GM157217-01 |
| Project ID | NIH-11021373 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients at risk of developing sepsis, particularly those with underlying health conditions or undergoing surgical procedures.
✕Less likely to help
Patients who are not at risk for sepsis or those with conditions unrelated to sepsis may not benefit from this research.
What this research studies
This research aims to develop sophisticated machine learning models that can analyze diverse clinical data, such as medical images and electronic health records, to identify early signs of sepsis. By leveraging large-scale datasets and advanced algorithms, the project seeks to improve the accuracy and efficiency of sepsis detection, which is crucial for timely medical intervention. The approach involves training foundation models that can recognize complex patterns in patient data, ultimately enhancing decision-making in clinical settings.
Why it matters
Potential benefit: If successful, this research could significantly reduce the risk of death from sepsis by enabling earlier diagnosis and treatment.
How similar studies have gone: Previous research has shown promise in using machine learning for early detection of various medical conditions, indicating potential success for this novel approach in sepsis detection.
Where this research is happening
-
University of California, San Diego
La Jolla, United States Active
Researchers
- Principal investigator: Xie, Pengtao — University of California, San Diego
- Study coordinator: Xie, Pengtao
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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.