SmokeBERT: A Bidirectional Encoder Representations From Transformers–Based Model for Quantitative Smoking History Extraction From Clinical Narratives to Improve Lung Cancer Screening
We developed SmokeBERT, a fine-tuned BERT-based model optimized for extracting detailed quantitative smoking histories. Future work includes evaluating performance on larger clinical data sets and developing a multilingual, language-agnostic version of SmokeBERT.
Authors
- Yunzheng Zhu (ORCID: https://orcid.org/0009-0006-9143-7137)
- William W.Y. Hsu (ORCID: https://orcid.org/0000-0002-5168-070X)
- Yongkang Xue (ORCID: https://orcid.org/0000-0002-6169-9631)
- Yannan Lin (ORCID: https://orcid.org/0000-0003-3514-6475)
- Ricky K. Taira (ORCID: https://orcid.org/0000-0001-9754-8993)
- YongKyung Oh (ORCID: https://orcid.org/0000-0002-8027-3433)
- Luoting Zhuang (ORCID: https://orcid.org/0000-0002-3714-3174)
- Denise R. Aberle (ORCID: https://orcid.org/0000-0002-8858-3401)
- Ashley E. Prosper (ORCID: https://orcid.org/0000-0001-8615-5246)
Institutions
- University of California, Los Angeles (US)
- Olive View-UCLA Medical Center (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1200/cci-25-00223
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00