Predicting when heart failure might occur using data from wearables and health records
Official title: Prediction of Heart Failure Onset using Multimodal Data Analysis, Deep Learning and Commercial Wearables
Funded at University of Michigan at Ann Arbor · NIH-10904833 · Data updated 2026-11-12
This study is looking to help people at risk of heart failure by using smart technology and health records to predict when it might happen, giving doctors a chance to step in and help up to a year in advance.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Michigan at Ann Arbor NIH-funded |
| Lab location | 1 site (Ann Arbor, United States) |
| Project start | September 1, 2021 |
| Project end | August 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $162,870 |
| NIH project number | 5K01HL155404-04 |
| Project ID | NIH-10904833 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals at risk of developing heart failure, particularly those with existing cardiovascular conditions or risk factors.
✕Less likely to help
Patients who are already diagnosed with advanced heart failure may not benefit from this predictive research.
What this research studies
This research aims to predict the onset of heart failure by analyzing various data sources, including electronic health records, electrocardiograms, and heart rate variability. By employing advanced deep learning techniques, the study seeks to enhance the accuracy of predictions, potentially identifying patients at risk up to 12 months in advance. Additionally, the research will explore the use of consumer wearables, like smartwatches, to gather relevant health data, making it more accessible for patients. The goal is to enable earlier interventions that could improve patient outcomes and experiences.
Why it matters
Potential benefit: If successful, this research could lead to earlier detection of heart failure, allowing for timely interventions that may improve patient health and quality of life.
How similar studies have gone: Other research has shown promise in using wearable technology and data analytics for predicting health outcomes, suggesting that this approach could be effective.
Where this research is happening
-
University of Michigan at Ann Arbor
Ann Arbor, United States Active
Researchers
- Principal investigator: Ansari, Sardar — University of Michigan at Ann Arbor
- Study coordinator: Ansari, Sardar
Related research
Other NIH-funded projects on the same topics.
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Predicting future heart failure with health records and wearables
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Using AI to Understand and Improve Treatment for a Type of Heart Failure
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.
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Using deep learning to improve heart failure care quality and reduce disparities.
This study is looking at how to improve care for people with heart failure, especially for Black individuals who often face more challenges, by using advanced technology to better understand their health records and find ways to provide better support and treatment.
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Using deep learning to improve lung ultrasound assessments for heart failure in emergency patients
This study is looking at how to make it easier and faster for doctors in the Emergency Department to check for heart failure in patients by using smart technology to analyze lung ultrasound images, helping you get the care you need more quickly.
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AI-supported heart failure diagnosis and management in Rwanda
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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.