Improving suicide risk identification using machine learning algorithms
Official title: Exploratory Research Project - ADAPT
Funded at Univ of Massachusetts Med Sch Worcester · NIH-11056016 · Data updated 2026-11-12
This study is working on a smart computer program that helps doctors spot people who might be at risk of suicide, making it easier for them to provide support in different healthcare settings.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of Massachusetts Med Sch Worcester NIH-funded |
| Lab location | 1 site (Worcester, United States) |
| Project start | April 5, 2023 |
| Project end | March 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $176,806 |
| NIH project number | 5P50MH129701-03 |
| Project ID | NIH-11056016 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with a history of mood disorders or those who have exhibited suicidal thoughts or behaviors.
✕Less likely to help
Patients who do not have any history of mental health issues or suicidal ideation may not benefit from this research.
What this research studies
This research focuses on enhancing a machine learning-based algorithm designed to identify individuals at risk of suicide. By systematically evaluating and improving the algorithm's ability to adapt to different healthcare settings, the project aims to ensure that it can be effectively used in clinical practice. The research team, composed of experts in predictive modeling and mental health, will utilize electronic health record data to refine the algorithm's accuracy and applicability. The goal is to create a user-friendly tool that can be integrated into existing healthcare systems to better predict and prevent suicide risk.
Why it matters
Potential benefit: If successful, this research could lead to more accurate identification of individuals at risk for suicide, ultimately saving lives through timely intervention.
How similar studies have gone: Previous research has shown promise in using machine learning for risk prediction in various health contexts, indicating potential success for this innovative approach.
Where this research is happening
-
Univ of Massachusetts Med Sch Worcester
Worcester, United States Active
Researchers
- Principal investigator: Liu, Feifan — Univ of Massachusetts Med Sch Worcester
- Study coordinator: Liu, Feifan
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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.