Understanding risks for early substance use in youth using machine learning
Official title: Using machine learning to accelerate our understanding of risks for early substance use among child-welfare and community youth
Funded at Kaiser Foundation Research Institute · NIH-11079604 · Data updated 2026-11-12
This study is looking at what might lead young people, especially those in child welfare, to start using drugs or alcohol early on, using smart computer techniques to find patterns in health records, so we can better help these teens and keep them safe.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Kaiser Foundation Research Institute NIH-funded |
| Lab location | 1 site (Oakland, UNITED STATES) |
| Project start | August 1, 2023 |
| Project end | May 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $596,416 |
| NIH project number | 5R01DA057214-03 |
| Project ID | NIH-11079604 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include adolescents aged 0-20 years, particularly those with experiences of child maltreatment or involvement in child welfare.
✕Less likely to help
Patients who are not adolescents or those without any history of substance use or child welfare involvement may not benefit from this research.
What this research studies
This research investigates the risk factors associated with early substance use among youth, particularly those involved in child welfare. By employing advanced machine learning techniques, the study aims to analyze data from electronic health records to identify unique predictors of substance use in this vulnerable population. The goal is to enhance screening methods and improve prevention strategies, ultimately leading to better outcomes for adolescents at risk. The research will focus on both child welfare youth and their non-child welfare counterparts to uncover common and distinct risk factors.
Why it matters
Potential benefit: If successful, this research could lead to improved prevention strategies and interventions for early substance use among at-risk youth.
How similar studies have gone: Other research has shown success in using machine learning to identify risk factors in various health contexts, suggesting a promising approach for this study.
Where this research is happening
-
Kaiser Foundation Research Institute
Oakland, United States Active
Researchers
- Principal investigator: Negriff, Sonya L — Kaiser Foundation Research Institute
- Study coordinator: Negriff, Sonya L
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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.