Using machine learning to better detect hypertrophic cardiomyopathy from health records
Official title: Improving the Detection of Hypertrophic Cardiomyopathy Using Machine Learning Applied to Electronic Health Record Data
Funded at University of Pennsylvania · NIH-10910166 · Data updated 2026-11-12
This study is working on using advanced computer technology to help find people who might have hypertrophic cardiomyopathy, a heart condition that often runs in families, even if they haven't been diagnosed yet, so they can get the care they need sooner.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pennsylvania NIH-funded |
| Lab location | 1 site (Philadelphia, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $172,832 |
| NIH project number | 5K23HL166961-02 |
| Project ID | NIH-10910166 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with a family history of hypertrophic cardiomyopathy or those showing symptoms related to heart conditions.
✕Less likely to help
Patients who have already been diagnosed and are receiving appropriate treatment for hypertrophic cardiomyopathy may not benefit directly from this research.
What this research studies
This research focuses on improving the detection of hypertrophic cardiomyopathy, a common inherited heart condition, by utilizing machine learning techniques applied to electronic health record data. The goal is to identify individuals who may have this condition but have not yet been diagnosed, allowing for earlier intervention and treatment. The study will develop and validate diagnostic algorithms that analyze various health data to enhance detection rates. By leveraging data from multiple institutions, the research aims to create a more accurate and sensitive model for identifying at-risk patients.
Why it matters
Potential benefit: If successful, this research could lead to earlier diagnosis and treatment for individuals with hypertrophic cardiomyopathy, potentially reducing the risk of severe complications such as heart failure and sudden death.
How similar studies have gone: Previous research has shown promise in using machine learning for improving diagnostic accuracy in various medical conditions, suggesting that this approach could be effective for hypertrophic cardiomyopathy as well.
Where this research is happening
-
University of Pennsylvania
Philadelphia, United States Active
Researchers
- Principal investigator: Reza, Nosheen — University of Pennsylvania
- Study coordinator: Reza, Nosheen
Related research
Other NIH-funded projects on the same topics.
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-
Using big biobanks to understand rare gene changes in hypertrophic cardiomyopathy
A large study will look at genetic data and health information from many people to better understand how rare gene changes influence hypertrophic cardiomyopathy and its risks.
-
Understanding heart disease in hypertrophic cardiomyopathy through heart tissue and blood analysis
Researchers are studying people with hypertrophic cardiomyopathy to see how genes and signaling in heart tissue and blood relate to disease progression and risk of serious heart problems.
-
Myosin binding protein C variants and heart disease
A doctor-scientist at the University of Michigan is studying how certain genetic changes in a heart protein (MYBPC3) cause hypertrophic cardiomyopathy and whether fixing the protein or its interactions could help people with this condition.
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A 12-month, multi-site trial tests whether the medicine trametinib can help infants with life-threatening heart failure due to Noonan syndrome and related genetic conditions.
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MEK inhibitor shows promise for serious infant heart disease in Noonan/RASopathies
A multi-site study tests whether the drug trametinib can help infants with severe heart muscle thickening caused by Noonan syndrome and related RASopathies.
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.