Data-driven restless multi-armed bandit to optimize public health interventions for diabetes and heart health
Official title: SCH: Advancing Public Health Intervention with Data-driven Restless Multi-Armed Bandit Framework
Funded at Georgia Institute of Technology · NIH-11418002 · Data updated 2026-11-12
A data-powered approach uses real-world health data to tailor and optimize public health interventions for adults with diabetes and related conditions.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Georgia Institute of Technology NIH-funded |
| Lab location | 1 site (Atlanta, United States) |
| Project start | September 15, 2025 |
| Project end | June 30, 2029 |
| Funding year | 2026 |
| Award (this year) | $293,997 |
| NIH project number | 5R01HL184139-02 |
| Project ID | NIH-11418002 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with diabetes or cardiovascular risk factors who would receive tailored public health interventions based on their data profiles.
✕Less likely to help
Patients not represented in the underlying data or whose conditions fall outside the targeted disease areas may see limited benefits.
What this research studies
This grant aims to build advanced computer models that decide which health interventions to deliver to which patients, using data from electronic health records and prior studies. The models will adapt over time to patient context, disease progression, and response to treatments to allocate resources efficiently. By focusing on diabetes, cardiovascular health, and maternal health, the project seeks to improve outcomes while making better use of limited healthcare resources. The work combines real-world data, clinical trials, and cohort studies to test and refine the decision-making framework in the U.S. healthcare system.
Why it matters
Potential benefit: If successful, it could improve timely treatment, personalized guidance, and resource allocation for patients with diabetes and cardiovascular risk.
How similar studies have gone: Prior studies using data-driven decision frameworks have shown promise in optimizing health interventions, though applying a contextual RMAB model to large real-world datasets is still emerging.
Where this research is happening
-
Georgia Institute of Technology
Atlanta, United States Active
Researchers
- Principal investigator: Wang, Kai — Georgia Institute of Technology
- Study coordinator: Wang, Kai
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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.