Leveraging natural language processing for efficient information extraction from breast cancer pathology reports: Single-institution study
Our results showed that the NLP model, BioBERT, had higher accuracy than regular expression, suggesting the importance of BioBERT in the processing of breast cancer pathology reports.
Authors
- Kui Son Choi (ORCID: https://orcid.org/0000-0001-5336-3874)
- Yeonho Choi
- Chong Woo Yoo
- Phillip Park (ORCID: https://orcid.org/0000-0002-8769-6383)
- Juyeon Hwang
- Hyun-Jin Kim (ORCID: https://orcid.org/0000-0003-4160-4815)
- Nayoung Han (ORCID: https://orcid.org/0000-0003-4796-8564)
- Heejung Chae (ORCID: https://orcid.org/0000-0003-3547-3521)
- Y. S. Park
Institutions
- Yonsei University (KR)
- Samsung (South Korea) (KR)
- National Cancer Center (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1371/journal.pone.0318726
- Citations
- 3
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 7.44