Using AI to improve cardiovascular risk prediction by considering social factors
Official title: AI2Equity: AI Integrating Social Determinants of Health to Advance Health Equity in Cardiovascular Risk Prediction
Funded at Univ of Massachusetts Med Sch Worcester · NIH-11076656 · Data updated 2026-11-12
This study is working on a smart computer program that looks at both health and social factors to better predict the risk of heart disease, especially for people from different racial and ethnic backgrounds and those with lower incomes, so we can help everyone get the care they need.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of Massachusetts Med Sch Worcester NIH-funded |
| Lab location | 1 site (Worcester, United States) |
| Project start | April 20, 2024 |
| Project end | March 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $632,835 |
| NIH project number | 5R01HL171599-02 |
| Project ID | NIH-11076656 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals from racial and ethnic minority groups or those with lower socioeconomic status who are at risk for cardiovascular disease.
✕Less likely to help
Patients who do not belong to the targeted racial/ethnic minorities or lower socioeconomic groups may not benefit directly from this research.
What this research studies
This research aims to develop an advanced AI model that integrates social determinants of health (SDOH) to enhance the prediction of cardiovascular disease (CVD) risk, particularly for racial and ethnic minorities and lower socioeconomic groups. By utilizing machine learning techniques, the study will analyze complex interactions between clinical and social factors that contribute to health disparities. The goal is to create a more equitable and interpretable risk prediction model that can inform targeted interventions to reduce these disparities. The research will involve collaboration among experts in AI, clinical care, and health equity, utilizing data from diverse patient populations.
Why it matters
Potential benefit: If successful, this research could lead to more accurate cardiovascular risk assessments and tailored interventions for underserved populations.
How similar studies have gone: Previous research has shown promise in using AI and machine learning to address health disparities, indicating that this approach could be effective.
Where this research is happening
-
Univ of Massachusetts Med Sch Worcester
Worcester, United States Active
Researchers
- Principal investigator: Liu, Feifan — Univ of Massachusetts Med Sch Worcester
- Study coordinator: Liu, Feifan
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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.