Application of Machine Learning to Auto-Code Injury Data in the e-CHIRPP System: Development and Evaluation Study
Shamir N Mukhi, Steven R McFaull, Wendy Thompson, Tim Beattie, Edward Mensah · 2025
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Description
Shamir Mukhi and co-authors test machine learning to code injury reports in a Canadian hospital system, in a study from 2025. The system has tracked injuries seen in emergency rooms since 1990 and holds millions of records. Staff have long coded the free-text accounts by hand, which is slow and delays findings.
The project built and judged models that assign codes on their own as a first step toward live use. Faster coding would let health teams spot risks sooner and shape prevention work with fresher data. The authors frame the work as a path of steady improvement rather than a one-time fix, with human review still in the loop.
Catalog record
| CREATOR | Shamir N Mukhi, Steven R McFaull, Wendy Thompson, Tim Beattie, Edward Mensah |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 35 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |