  Accurate extraction of breast cancer patients' phenotypes is important for
clinical decision support and clinical research. Current models do not take
full advantage of cancer domain-specific corpus, whether pre-training
Bidirectional Encoder Representations from Transformer model on cancer-specific
corpus could improve the performances of extracting breast cancer phenotypes
from texts data remains to be explored. The objective of this study is to
develop and evaluate the CancerBERT model for extracting breast cancer
phenotypes from clinical texts in electronic health records. This data used in
the study included 21,291 breast cancer patients diagnosed from 2010 to 2020,
patients' clinical notes and pathology reports were collected from the
University of Minnesota Clinical Data Repository (UMN). Results: About 3
million clinical notes and pathology reports in electronic health records for
21,291 breast cancer patients were collected to train the CancerBERT model. 200
pathology reports and 50 clinical notes of breast cancer patients that contain
9,685 sentences and 221,356 tokens were manually annotated by two annotators.
20% of the annotated data was used as a test set. Our CancerBERT model achieved
the best performance with macro F1 scores equal to 0.876 (95% CI, 0.896-0.902)
for exact match and 0.904 (95% CI, 0.896-0.902) for the lenient match. The NER
models we developed would facilitate the automated information extraction from
clinical texts to further help clinical decision support. Conclusions and
Relevance: In this study, we focused on the breast cancer-related concepts
extraction from EHR data and obtained a comprehensive annotated dataset that
contains 7 types of breast cancer-related concepts. The CancerBERT model with
customized vocabulary could significantly improve the performance for
extracting breast cancer phenotypes from clinical texts.
