Using advanced machine learning to improve understanding of missing health data in cardiovascular and obesity studies
Official title: Bayesian machine learning for complex missing data and causal inference with a focus on cardiovascular and obesity studies
Funded at University of Florida · NIH-11045724 · Data updated 2026-11-12
This study is working on new ways to use advanced computer techniques to fill in missing information in health records, especially for heart and weight-related issues, so that patients can get better treatments based on a clearer understanding of their health data.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Florida NIH-funded |
| Lab location | 1 site (Gainesville, United States) |
| Project start | March 1, 2023 |
| Project end | February 28, 2027 |
| Funding year | 2025 |
| Award (this year) | $486,028 |
| NIH project number | 5R01HL166324-03 |
| Project ID | NIH-11045724 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with cardiovascular conditions or obesity who have electronic health records that may contain missing data.
✕Less likely to help
Patients without cardiovascular conditions or obesity, or those not represented in electronic health records, may not benefit from this research.
What this research studies
This research focuses on developing advanced Bayesian machine learning techniques to address the challenges of missing data in electronic health records (EHRs), particularly in the context of cardiovascular and obesity studies. By utilizing Bayesian nonparametric methods, the project aims to improve causal inference and better understand the relationships between various health outcomes and treatments. Patients may benefit from enhanced analysis of their health data, leading to more effective interventions and treatment strategies. The research will also explore how different factors mediate treatment effects, which can inform future clinical practices.
Why it matters
Potential benefit: If successful, this research could lead to more accurate health assessments and improved treatment strategies for patients with cardiovascular issues and obesity.
How similar studies have gone: Other research has shown promise in using Bayesian methods for handling missing data, indicating that this approach could be effective in this context as well.
Where this research is happening
-
University of Florida
Gainesville, United States Active
Researchers
- Principal investigator: Daniels, Michael J — University of Florida
- Study coordinator: Daniels, Michael J
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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.