Personalized prevention for perinatal depression using machine learning
Official title: SCH: Machine learning for personalized preventative intervention in perinatal depression
Funded at Carnegie-Mellon University · NIH-11175529 · Data updated 2026-11-12
Using machine learning, this project will match pregnant people and new parents with the right preventive mental-health support before depression starts.
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 | 2025 |
| Award (this year) | $257,968 |
| NIH project number | 5R01MH139097-02 |
| Project ID | NIH-11175529 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal participants are pregnant people or new parents at risk for perinatal depression, including those with prior depression, current symptoms, or other known risk factors.
✕Less likely to help
People who are not pregnant or postpartum, those already experiencing severe, immediate depression requiring urgent treatment, or those unwilling to share health information may not receive direct benefit from this prevention-focused work.
What this research studies
You may be asked to share health, behavior, or symptom information and to join interviews about your pregnancy or postpartum experience while researchers combine that with existing medical data to train computer models. The team will work closely with clinicians and people with lived experience to make sure recommendations are clear, fair, and relevant. The project will build tools that suggest which preventive supports to try first and update choices over time using adaptive experiments. The aim is to create scalable, understandable, and individualized prevention options that could be offered in routine care.
Why it matters
Potential benefit: If successful, this could help identify people at risk earlier and direct them to the specific support most likely to prevent perinatal depression.
How similar studies have gone: Some prior work has used machine learning to predict postpartum depression risk, but applying ML to select and adapt personalized preventive treatments is relatively new and not yet proven at scale.
Where this research is happening
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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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Machine learning to tailor and prevent perinatal depression for pregnant and new mothers
A machine learning project at Carnegie Mellon aims to find personalized, scalable ways to prevent perinatal depression in pregnant people and new mothers.
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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.
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.