Using machine learning to improve treatment for heart rhythm disorders
Official title: Machine Learning for Ventricular Arrhythmias
Funded at Stanford University · NIH-10827474 · Data updated 2026-11-12
This study is looking to help people with serious heart rhythm issues, like ventricular tachycardia and fibrillation, by using advanced technology to figure out which treatments, like medications or procedures, will work best for them based on their unique health information.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | July 1, 2023 |
| Project end | June 30, 2027 |
| Funding year | 2024 |
| Award (this year) | $606,939 |
| NIH project number | 5R01HL162260-02 |
| Project ID | NIH-10827474 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients diagnosed with ventricular tachycardia or fibrillation who are at risk for cardiac arrest.
✕Less likely to help
Patients who do not have ventricular arrhythmias or those who are not at risk for cardiac complications may not benefit from this research.
What this research studies
This research focuses on improving the management of patients with ventricular tachycardia (VT) and fibrillation, which are serious heart rhythm disorders. By utilizing machine learning techniques on large datasets, the project aims to predict which patients are most likely to benefit from specific therapies, such as anti-arrhythmic medications or ablation procedures. The approach combines data from various sources, including clinical data and non-invasive imaging, to create a personalized treatment plan for each patient. The research will validate its findings using large external registries to ensure accuracy and effectiveness.
Why it matters
Potential benefit: If successful, this research could lead to more effective and personalized treatments for patients with ventricular arrhythmias, potentially reducing hospitalizations and improving quality of life.
How similar studies have gone: Other research has shown promise in using machine learning for predicting treatment responses in various medical conditions, suggesting that this approach could be effective for ventricular arrhythmias as well.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Narayan, Sanjiv M — Stanford University
- Study coordinator: Narayan, Sanjiv M
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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.