Introducing mCODEGPT as a zero-shot information extraction from clinical free text data tool for cancer research
Our approach demonstrates that LLMs, when guided by structured prompting, can accurately extract complex clinical information without the need for expert-labeled data. This method has the potential to harness unstructured data for advancing cancer research.
Authors
- Qi Long (ORCID: https://orcid.org/0000-0003-0660-5230)
- Kai Zhang (ORCID: https://orcid.org/0000-0002-4519-609X)
- Tongtong Huang
- Travis John Osterman (ORCID: https://orcid.org/0000-0002-2841-8121)
- Xiaoqian Jiang (ORCID: https://orcid.org/0000-0001-9933-2205)
- Bradley Malin (ORCID: https://orcid.org/0000-0003-3040-5175)
Institutions
- The University of Texas Health Science Center (US)
- University of Pennsylvania (US)
- Vanderbilt University Medical Center (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-10-15
- DOI
- https://doi.org/10.1038/s43856-025-01116-x
- Citations
- 3
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 2.42
Funders
- Cancer Prevention and Research Institute of Texas
- University of Texas Health Science Center at Houston
- National Cancer Institute