Introducing mCODEGPT as a zero-shot information extraction from clinical free text data tool for cancer research
Plain-language summary
Clinical notes about cancer patients hold valuable information, but extracting it in a structured form is difficult, and traditional natural language processing methods require extensive expert annotation and model training for each type of data. This paper introduces mCODEGPT, a tool for zero-shot information extraction from clinical free text, meaning it can pull structured data from notes without task-specific training. The aim is a more efficient and accurate way to standardize and structure clinical text for cancer research.
Read the full paper (DOI: 10.1038/s43856-025-01116-x).
This is a plain-language summary written for discoverability; the authoritative version is the published paper. Part of Travis Osterman's peer-reviewed publications.