Heart failure endotypes found through ethical multi-modal AI and molecular data
Official title: HF-ETIOLOGY: Heart Failure Endotypes from Ethical, Multi-Modal AI-driven and Molecular/Phenotypic Data Integration Enabled Discovery
Funded at University of Washington · NIH-11607941 · Data updated 2026-11-12
A multi-modal AI project using clinical, molecular, and behavioral data to identify distinct heart failure subtypes and guide future treatments.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Washington NIH-funded |
| Lab location | 1 site (Seattle, United States) |
| Project start | July 17, 2026 |
| Project end | August 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $237,632 |
| NIH project number | 7OT2OD038083-02 |
| Project ID | NIH-11607941 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients with heart failure or at risk for HF who contribute data across clinical, molecular, and behavioral domains.
✕Less likely to help
Some patients may not directly benefit if they do not share the data or if endotypes do not translate into actionable therapies.
What this research studies
This project aims to create an ethical, patient-centered AI framework that combines phenotypic, multi-omic, and social-behavioral information to define heart failure endotypes. It will develop and test new AI models that can learn from diverse data types, including longitudinal clinical records, networked molecular data, and Social Determinants of Health. The work emphasizes co-design with stakeholders and aims to establish data and model practices that support reproducibility and transparency. By linking endotypes to potential drug targets and pathways, the research could inform more personalized treatment approaches in heart failure.
Why it matters
Potential benefit: If successful, patients with heart failure could receive more personalized care based on their endotype.
How similar studies have gone: Past efforts show promise for multi-modal data approaches, but this project tackles co-design and integration with behavioral factors, which is more novel.
Where this research is happening
-
University of Washington
Seattle, United States Active
Researchers
- Principal investigator: Luo, Yuan — University of Washington
- Study coordinator: Luo, Yuan
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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.