Using electronic health records and community data to identify pregnant individuals at risk of depression
Official title: Integration of electronic medical records and neighborhood contextual indicators into machine learning strategies for identifying pregnant individuals at risk of depression in underserved communities
Funded at University of Illinois at Chicago · NIH-10741143 · Data updated 2026-11-12
This study is looking to improve how we predict depression during and after pregnancy for women of color, especially Non-Hispanic Black women, by using health records and community information to better understand their needs and provide more helpful support.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Illinois at Chicago NIH-funded |
| Lab location | 1 site (Chicago, UNITED STATES) |
| Project start | September 19, 2023 |
| Project end | August 31, 2025 |
| Funding year | 2023 |
| Award (this year) | $419,357 |
| NIH project number | 1R21HD110779-01A1 |
| Project ID | NIH-10741143 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include pregnant individuals, particularly Minoritized Women of Color, who may be at risk for perinatal depression.
✕Less likely to help
Patients who are not pregnant or who do not identify as Minoritized Women of Color may not receive benefit from this research.
What this research studies
This research aims to enhance the prediction of depression during pregnancy and the postpartum period by utilizing electronic medical records and community contextual indicators. It focuses on Minoritized Women of Color, particularly Non-Hispanic Black Women, who are at a higher risk for perinatal depression. The study employs machine learning models that are designed to be interpretable and to mitigate biases in predictions based on socio-demographic factors. By incorporating social determinants of health, the research seeks to provide more accurate assessments and insights for clinical interventions.
Why it matters
Potential benefit: If successful, this research could lead to improved identification and support for pregnant individuals at risk of depression, ultimately enhancing maternal mental health outcomes.
How similar studies have gone: Other research has shown success in using machine learning and social determinants of health to improve health outcomes, making this approach promising yet still innovative in the context of perinatal depression.
Where this research is happening
-
University of Illinois at Chicago
Chicago, United States Active
Researchers
- Principal investigator: Penalver Bernabe, Beatriz — University of Illinois at Chicago
- Study coordinator: Penalver Bernabe, Beatriz
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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.