Improving early prediction and decision-making for sepsis using AI and human collaboration
Official title: SCH: Improving Early Prediction and Decision-Making for Sepsis with Human-AI Collaboration
Funded at Ohio State University · NIH-11140392 · Data updated 2026-11-12
This study is working on a smart computer system that helps doctors spot the early signs of sepsis, a serious condition, by using patient health records to make better predictions and decisions, ultimately aiming to improve care for those at risk.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Ohio State University NIH-funded |
| Lab location | 1 site (Columbus, UNITED STATES) |
| Project start | July 17, 2024 |
| Project end | May 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $294,506 |
| NIH project number | 5R01AI188576-02 |
| Project ID | NIH-11140392 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients presenting with symptoms indicative of sepsis in emergency departments or hospital wards.
✕Less likely to help
Patients who are not at risk for sepsis or those who are already in critical care settings may not benefit from this research.
What this research studies
This research focuses on enhancing the early prediction and decision-making processes for sepsis, a life-threatening condition, by developing a Human-Centered Artificial Intelligence (HCAI) system. The approach involves creating a comprehensive database of patient Electronic Health Records (EHRs) to train machine learning models that can predict sepsis risk more accurately. The system aims to provide actionable insights to physicians, allowing them to make informed decisions in high-pressure situations. By incorporating uncertainty quantification and interactive exploration, the research seeks to improve clinical outcomes for patients at risk of sepsis.
Why it matters
Potential benefit: If successful, this research could significantly increase the chances of early sepsis diagnosis, potentially improving survival rates for affected patients.
How similar studies have gone: Previous research has shown promise in using AI for clinical decision-making, but this specific approach of integrating human collaboration with AI in emergency settings is relatively novel.
Where this research is happening
-
Ohio State University
Columbus, United States Active
Researchers
- Principal investigator: Zhang, Ping — Ohio State University
- Study coordinator: Zhang, Ping
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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.