Machine learning to tailor and prevent perinatal depression for pregnant and new mothers
Official title: SCH: Machine learning for personalized preventative intervention in perinatal depression
Funded at Carnegie-Mellon University · NIH-11415592 · Data updated 2026-11-12
A machine learning project at Carnegie Mellon aims to find personalized, scalable ways to prevent perinatal depression in pregnant people and new mothers.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Carnegie-Mellon University NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project start | September 1, 2024 |
| Project end | June 30, 2028 |
| Funding year | 2026 |
| Award (this year) | $250,988 |
| NIH project number | 5R01MH139097-03 |
| Project ID | NIH-11415592 on NIH RePORTER |
Who could benefit from this research
✓Could help
Pregnant individuals and new mothers who are at risk for perinatal depression and would benefit from tailored preventive interventions.
✕Less likely to help
People without risk factors for perinatal depression or those not seeking preventive care may not benefit from this approach.
What this research studies
This project uses historical data and new input from clinicians and lived experience to develop interpretable ML methods that pinpoint who would benefit most from specific preventive interventions during the perinatal period. The team will design methods to guide randomized experiments and determine initial allocation policies for interventions. It will also incorporate insights from perinatal women to ensure the tools fit real-world needs and values. The research emphasizes making prevention smarter, more accessible, and aligned with patients’ lived experiences.
Why it matters
Potential benefit: If successful, it could lead to earlier, more personalized prevention strategies that reduce the risk and impact of perinatal depression.
How similar studies have gone: Prior work shows ML can improve prediction of mental health needs, but integrating domain expertise and lived experience into personalized prevention is an emerging area.
Where this research is happening
-
Carnegie-Mellon University
Pittsburgh, United States Active
Researchers
- Principal investigator: Wilder, Bryan — Carnegie-Mellon University
- Study coordinator: Wilder, Bryan
Related research
Other NIH-funded projects on the same topics.
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Couples-based approach to prevent perinatal depression in rural India
A new, family-focused program helps new mothers and their husbands address perinatal depression in rural India using trained lay counselors.
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Couples-based approach to prevent perinatal depression in rural India
A pilot program in rural India tests a couples-based, lay-counselor–delivered approach to prevent perinatal depression for new mothers and their partners.
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Online sleep therapy to prevent depression during and after pregnancy
This project will see whether an online cognitive behavioral therapy for insomnia can help prevent depression during pregnancy and in the first year after childbirth for pregnant people with insomnia.
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Understanding how gut and body signals interact to influence perinatal depression
A study of pregnant people exploring how the gut microbiome and body signals contribute to depression during and after pregnancy.
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Couples program to prevent depression during pregnancy and after birth in rural India
A couples-based program to help first-time pregnant women and their partners in rural India lower the chance of depression during pregnancy and after childbirth.
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IMAGINE digital group program to prevent perinatal depression
This pilot offers a mobile group-based cognitive behavioral program called IMAGINE to help pregnant and postpartum people lower their chance of developing depression.
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.