Extracting lung cancer staging descriptors from pathology reports: A generative language model approach
This study demonstrated that training GLMs with deductive datasets can improve information extraction performance, and GLMs with a relatively small number of parameters at approximately seven billion can achieve high performance in this problem. The proposed GLM-based information extraction method is expected to be useful in clinical decision-making support, lung cancer staging and research.
Authors
- Jin‐Haeng Chung
- Borham Kim (ORCID: https://orcid.org/0000-0002-6914-5721)
- Leonard Sunwoo (ORCID: https://orcid.org/0000-0003-0374-8658)
- Hyeongmin Cho (ORCID: https://orcid.org/0000-0002-7256-4812)
- Sooyoung Yoo (ORCID: https://orcid.org/0000-0001-8620-4925)
- Sang‐Hwan Kim (ORCID: https://orcid.org/0000-0003-0996-6912)
- Sowon Jang (ORCID: https://orcid.org/0000-0003-3320-1557)
- Seok Kim (ORCID: https://orcid.org/0000-0003-4996-8613)
- Sejin Nam (ORCID: https://orcid.org/0000-0003-4786-2241)
- Donghyoung Lee
Institutions
- Seoul National University (KR)
- Seoul National University Bundang Hospital (KR)
Publication Details
- Journal
- Journal of Biomedical Informatics
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1016/j.jbi.2024.104720
- Citations
- 18
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 4.21
Funders
- Ministry of Health and Welfare
- Korea Health Industry Development Institute