Using machine learning to create personalized prevention strategies for perinatal depression
Official title: SCH: Machine learning for personalized preventative intervention in perinatal depression
Funded at Carnegie-Mellon University · NIH-11060670 · Data updated 2026-11-12
This study is looking to use smart technology to create personalized support for people dealing with perinatal depression, helping to figure out who might need specific help the most, so they can get the best care during pregnancy and after.
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 | 2024 |
| Award (this year) | $277,767 |
| NIH project number | 1R01MH139097-01 |
| Project ID | NIH-11060670 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include pregnant individuals who are at risk for perinatal depression.
✕Less likely to help
Patients who are not pregnant or who do not have risk factors for perinatal depression may not benefit from this research.
What this research studies
This research aims to harness machine learning to develop tailored preventative interventions for individuals experiencing perinatal depression, a condition affecting about 15% of pregnant individuals. By analyzing historical data and incorporating insights from both healthcare professionals and those with lived experiences, the project seeks to identify which patients would benefit most from specific interventions. The approach includes creating algorithms that can predict risk factors and guide personalized treatment plans, ultimately improving mental health care during the perinatal period.
Why it matters
Potential benefit: If successful, this research could lead to more effective and personalized prevention strategies for perinatal depression, potentially reducing its incidence and associated risks.
How similar studies have gone: Previous research has shown promise in using machine learning for mental health interventions, indicating potential success for this novel approach.
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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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.