Accelerated curation of checkpoint inhibitor-induced colitis cases from electronic health records
Plain-language summary
The authors built an automated pipeline using a natural-language-processing model (BERT) to find electronic health record notes documenting colitis caused by immune checkpoint inhibitor cancer therapy, a step needed to assemble training data for predictive models. The multi-stage pipeline segmented long notes, filtered out likely false positives, and highlighted the colitis-relevant text, identifying ICI-colitis notes with 84% precision while cutting the manual chart-review workload by 75%. Because manual curation from EHR notes is burdensome, the approach could accelerate research and be adapted to other clinical topics.
Read the full paper (DOI: 10.1093/jamiaopen/ooad017).
This is a plain-language summary written for discoverability; the authoritative version is the published paper. Part of Travis Osterman's peer-reviewed publications.