Using machine learning to detect cognitive impairment early in older adults
Official title: Integrating eSAGE with EHR data using machine learning for the early detection and monitoring of cognitive impairment in individuals including those underserved
Funded at Ohio State University · NIH-11077787 · Data updated 2026-11-12
This study is testing a new tool that uses health records and smart technology to help find signs of memory problems, like Alzheimer's, especially in communities that often don't get the care they need, making it easier for everyone to check their own brain health.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Ohio State University NIH-funded |
| Lab location | 1 site (Columbus, UNITED STATES) |
| Project start | May 1, 2024 |
| Project end | February 28, 2029 |
| Funding year | 2025 |
| Award (this year) | $738,977 |
| NIH project number | 5R01AG082044-02 |
| Project ID | NIH-11077787 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals aged 65 and older, particularly those from underserved racial and ethnic backgrounds.
✕Less likely to help
Patients under the age of 65 or those without cognitive impairment may not benefit from this research.
What this research studies
This research aims to integrate electronic health record (EHR) data with a new tool called eSAGE to improve the early detection and monitoring of cognitive impairment, particularly in underserved populations. By employing machine learning techniques, the project seeks to identify individuals at risk for Alzheimer's disease and mild cognitive impairment more effectively. The approach focuses on creating accessible, self-administered tests that can be used without supervision, making it easier for patients to participate in their own cognitive health assessments. The research also addresses disparities in healthcare access among different racial and ethnic groups, aiming to provide equitable solutions for early diagnosis.
Why it matters
Potential benefit: If successful, this research could lead to earlier detection of cognitive impairment, allowing for timely interventions and improved management of Alzheimer's disease.
How similar studies have gone: Previous research has shown promise in using machine learning for cognitive assessment, indicating that this approach could be effective in improving early detection.
Where this research is happening
-
Ohio State University
Columbus, United States Active
Researchers
- Principal investigator: Ning, Xia — Ohio State University
- Study coordinator: Ning, Xia
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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.