Using advanced algorithms to detect adverse medical events from electronic health records
Official title: AE2Vec: Medical concept embedding and time-series analysis for automated adverse event detection
Funded at Northwestern University at Chicago · NIH-10913344 · Data updated 2026-11-12
This study is working on a new way to quickly spot any problems that might happen from medical treatments, like medications or devices, by using smart technology to look at health records, so that patients can feel safer and get better care.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Northwestern University at Chicago NIH-funded |
| Lab location | 1 site (Chicago, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $47,912 |
| NIH project number | 5F31LM014201-02 |
| Project ID | NIH-10913344 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include patients undergoing treatment with drugs or devices that have a risk of adverse events, particularly those recorded in electronic health records.
✕Less likely to help
Patients who are not receiving any medical treatments or those whose data is not captured in electronic health records may not benefit from this research.
What this research studies
This research focuses on improving the detection of adverse events related to medical treatments, such as drugs and devices, by utilizing automated methods that analyze electronic health records (EHRs). The study aims to overcome the limitations of current manual reporting systems, which are often slow and incomplete. By embedding medical concepts and analyzing time-series data, the researchers hope to create a more efficient surveillance system that can identify adverse events in real-time. This could lead to better patient safety and more effective healthcare interventions.
Why it matters
Potential benefit: If successful, this research could significantly enhance patient safety by enabling quicker identification and response to adverse medical events.
How similar studies have gone: Previous research has shown promise in using automated methods for adverse event detection, indicating that this approach could lead to significant advancements in patient safety.
Where this research is happening
-
Northwestern University at Chicago
Chicago, United States Active
Researchers
- Principal investigator: Tran, Steven Duc — Northwestern University at Chicago
- Study coordinator: Tran, Steven Duc
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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.