Using machine learning to improve cardiovascular care based on clinical trial data
Official title: Translating Personalized Inference from Randomized Clinical Trials to Real-World Cardiovascular Care
Funded at Yale University · NIH-11053517 · Data updated 2026-11-12
This study is looking at how we can use computer technology to help doctors create personalized treatment plans for heart patients by analyzing data from different clinical trials, so that the care you receive is more tailored to your unique needs.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | March 15, 2024 |
| Project end | February 28, 2029 |
| Funding year | 2025 |
| Award (this year) | $670,000 |
| NIH project number | 5R01HL167858-02 |
| Project ID | NIH-11053517 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with cardiovascular conditions, such as coronary artery disease or heart failure, who may benefit from personalized treatment approaches.
✕Less likely to help
Patients with non-cardiovascular conditions or those who do not have access to the necessary healthcare systems may not benefit from this research.
What this research studies
This research investigates how machine learning can be applied to data from randomized clinical trials to create personalized treatment strategies for cardiovascular patients. By analyzing clinical characteristics from diverse patient populations, the study aims to develop predictive models that can better inform real-world cardiovascular care. The research will utilize participant data from multiple clinical trials and simulate treatment effects to evaluate the effectiveness of these models in various healthcare settings. Ultimately, the goal is to bridge the gap between clinical trial findings and everyday patient care.
Why it matters
Potential benefit: If successful, this research could lead to more tailored and effective cardiovascular treatments for patients based on their individual characteristics.
How similar studies have gone: Previous research has shown promise in using machine learning for personalized medicine, indicating potential success for this novel approach in cardiovascular care.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Khera, Rohan — Yale University
- Study coordinator: Khera, Rohan
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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.