DeepHIV-AoU: A smart approach to predicting and preventing HIV with All of Us data
Official title: DeepHIV-AoU: A Multi-modal Deep Learning Framework Leveraging the All of Us Research Program for HIV Precision Prevention
Funded at University of Texas Hlth Sci Ctr Houston · NIH-11470991 · Data updated 2026-11-12
A new AI-powered tool will use health data from many people to improve HIV prevention by predicting who is at higher risk and which interventions might work best.
Quick facts
| Grant type | R21 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, 2026 |
| Project end | August 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $200,268 |
| NIH project number | 1R21LM015664-01 |
| Project ID | NIH-11470991 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are adults connectable to All of Us data who are at risk of HIV acquisition and may benefit from tailored prevention strategies.
✕Less likely to help
Individuals not at risk of HIV or those without access to the necessary data streams may not benefit from this tool.
What this research studies
This project uses a multi-modal deep learning framework that combines electronic health records, genetic information, behavioral surveys, and other health drivers from the All of Us program to create a dynamic HIV acquisition risk score and targets for prevention. The work focuses on making predictions that are interpretable for clinicians and patients, with attention-based maps to show which factors drive the estimates. By testing its utility across varied population groups, the study aims to help tailor PrEP strategies and support persistence in real-world settings. The grant envisions rigorous evaluation of model parity, clinical usefulness, and potential impact on ending the HIV epidemic.
Why it matters
Potential benefit: If successful, the tool could help clinicians personalize HIV prevention strategies and improve PrEP uptake and persistence.
How similar studies have gone: Previous multi-modal AI efforts show promise in risk prediction, but this project adds temporal depth and population-wide generalization using AoU data.
Where this research is happening
-
University of Texas Hlth Sci Ctr Houston
Houston, United States Active
Researchers
- Principal investigator: Yaseen, Ashraf — University of Texas Hlth Sci Ctr Houston
- Study coordinator: Yaseen, Ashraf
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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.