Using AI to Understand and Improve Treatment for a Type of Heart Failure
Official title: Deep learning Based Phenotyping and Treatment Optimization for Heart Failure with Preserved Ejection Fraction
Funded at Massachusetts General Hospital · NIH-11044114 · Data updated 2026-11-12
This project uses advanced computer methods to better understand different forms of heart failure with preserved ejection fraction, aiming to find the most effective treatments for each person.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Massachusetts General Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | March 15, 2022 |
| Project end | February 28, 2027 |
| Funding year | 2025 |
| Award (this year) | $590,310 |
| NIH project number | 5R01HL159183-04 |
| Project ID | NIH-11044114 on NIH RePORTER |
Who could benefit from this research
✓Could help
This research focuses on understanding heart failure with preserved ejection fraction, particularly for individuals whose condition has been difficult to treat effectively.
✕Less likely to help
Patients without heart failure with preserved ejection fraction would not directly benefit from this specific research.
What this research studies
Heart failure with preserved ejection fraction (HFpEF) is a serious and growing health issue, especially among older adults and those with conditions like obesity and diabetes. Despite many past efforts, effective treatments for HFpEF have been hard to find, partly because it affects people in many different ways. This project plans to use powerful artificial intelligence (AI) to sort out these different forms of HFpEF. By understanding the unique characteristics of each patient's condition, we hope to discover more personalized and effective treatment plans. This approach could lead to better outcomes for those living with this challenging heart condition.
Why it matters
Potential benefit: If successful, this work could lead to more personalized and effective treatments for patients with heart failure with preserved ejection fraction, potentially improving their survival and quality of life.
How similar studies have gone: While previous large-scale clinical trials for HFpEF have often yielded neutral results, this project introduces a novel big data and AI approach to identify distinct patient subtypes, which is a promising new direction.
Where this research is happening
-
Massachusetts General Hospital
Boston, United States Active
Researchers
- Principal investigator: Li, Quanzheng — Massachusetts General Hospital
- Study coordinator: Li, Quanzheng
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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.