Using AI to Understand and Improve Treatment for a Type of Heart Failure
Deep learning Based Phenotyping and Treatment Optimization for Heart Failure with Preserved Ejection Fraction
['FUNDING_R01'] · MASSACHUSETTS GENERAL HOSPITAL · NIH-11044114
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
| Phase | ['FUNDING_R01'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | MASSACHUSETTS GENERAL HOSPITAL (nih funded) |
| Locations | 1 site (BOSTON, UNITED STATES) |
| Trial ID | NIH-11044114 on ClinicalTrials.gov |
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.
Who could benefit from this research
Good fit: This research focuses on understanding heart failure with preserved ejection fraction, particularly for individuals whose condition has been difficult to treat effectively.
Not a fit: Patients without heart failure with preserved ejection fraction would not directly benefit from this specific research.
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 performed: 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
BOSTON, UNITED STATES
- MASSACHUSETTS GENERAL HOSPITAL — BOSTON, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: LI, QUANZHENG — MASSACHUSETTS GENERAL HOSPITAL
- Study coordinator: LI, QUANZHENG
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 below.