Creating a machine-learning tool to predict opioid overdose risk
Developing and Evaluating a Machine-Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE)
This study is working on a smart tool that helps doctors find people who are at high risk for opioid overdose and addiction, so they can offer better support and care to those who need it most.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pittsburgh at Pittsburgh NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project ID | NIH-11084571 on NIH RePORTER |
What this research studies
This research focuses on developing a machine-learning platform that identifies individuals at high risk for opioid overdose and opioid use disorder (OUD). By analyzing various data sources, including electronic health records, the project aims to improve the accuracy of risk predictions compared to current methods. The goal is to create a clinical decision tool that healthcare providers can use to better target interventions for those most in need. This innovative approach seeks to address significant gaps in existing risk assessment strategies.
Who could benefit from this research
Good fit: Ideal candidates for this research include individuals who are at high risk for opioid overdose or have been diagnosed with opioid use disorder.
Not a fit: Patients who do not have any history of opioid use or overdose may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to more effective identification and intervention for individuals at risk of opioid overdose, potentially saving lives.
How similar studies have performed: Previous research has shown that machine-learning algorithms can significantly improve risk prediction for opioid-related issues, indicating a promising direction for this project.
Where this research is happening
Pittsburgh, United States
- University of Pittsburgh at Pittsburgh — Pittsburgh, United States (Active)
Researchers
- Principal investigator: Lo-Ciganic, Wei-Hsuan — University of Pittsburgh at Pittsburgh
- Study coordinator: Lo-Ciganic, Wei-Hsuan
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 below.