Using machine learning to analyze archived data on developmental psychiatric disorders
Official title: The promise of machine learning for novel approaches to archived developmental data
Funded at University of Pittsburgh at Pittsburgh · NIH-10949256 · Data updated 2026-11-12
This study is looking at how machine learning can help us understand and improve treatment for young people with depression by analyzing data collected from childhood into their 20s, so we can find out what helps or hurts their recovery.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pittsburgh at Pittsburgh NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project start | August 1, 2024 |
| Project end | July 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $234,231 |
| NIH project number | 1R21MH137601-01 |
| Project ID | NIH-10949256 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals who experienced depressive disorders starting in childhood or adolescence.
✕Less likely to help
Patients with depressive disorders that began in adulthood may not receive direct benefits from this research.
What this research studies
This research focuses on utilizing machine learning algorithms to analyze extensive longitudinal data on juvenile-onset depression and its impact on young patients. By examining data collected from ages 7 to the late 20s, the study aims to identify key risk and protective factors that influence the course of depressive disorders. The approach involves developing customizable algorithms that can process complex datasets to yield insights that may improve understanding and treatment of these conditions. Patients may benefit from the findings that could lead to better diagnostic and therapeutic strategies for depression.
Why it matters
Potential benefit: If successful, this research could enhance the understanding of juvenile-onset depression and lead to improved treatment options for affected individuals.
How similar studies have gone: Previous research has shown promise in using machine learning to analyze psychiatric data, indicating that this approach could yield valuable insights.
Where this research is happening
-
University of Pittsburgh at Pittsburgh
Pittsburgh, United States Active
Researchers
- Principal investigator: Kovacs, Maria — University of Pittsburgh at Pittsburgh
- Study coordinator: Kovacs, Maria
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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.