Extracting comprehensive clinical information for breast cancer using deep learning methods
Abstract
Objective Breast cancer is the most common malignant tumor among women. The diagnosis and treatment information of breast cancer patients is abundant in multiple types of clinical fields, including clinicopathological data, genotype and phenotype information, treatment information, and prognosis information. However, current studies are mainly focused on extracting information from one specific type of clinical field. This study defines a comprehensive information model to represent the whole-course clinical information of patients. Furthermore, deep learning approaches are used to extract the concepts and their attributes from clinical breast cancer documents by fine-tuning pretrained Bidirectional Encoder Representations from Transformers (BERT) language models.Materials and methods The clinical corpus that was used in this study was from one 3A cancer hospital in China, consisting of the encounter notes, operation records, pathology notes, radiology notes, progress notes and discharge summaries of 100 breast cancer patients. Our system consists of two components: a named entity recognition (NER) component and a relation recognition component. For each component, we implemented deep learning-based approaches by fine-tuning BERT, which outperformed other state-of-the-art methods on multiple natural language processing (NLP) tasks. A clinical language model is first pretrained using BERT on a large-scale unlabeled corpus of Chinese clinical text. For NER, the context embeddings that were pretrained using BERT were used as the input features of the Bi-LSTM-CRF (Bidirectional long-short-memory-conditional random fields) model and were fine-tuned using the annotated breast cancer notes. Furthermore, we proposed an approach to fine-tune BERT for relation extraction. It was considered to be a classification problem in which the two entities that were mentioned in the input sentence were replaced with their semantic types.Results Our best-performing system achieved F1 scores of 93.53% for the NER and 96.73% for the relation extraction. Additional evaluations showed that the deep learning-based approaches that fine-tuned BERT did outperform the traditional Bi-LSTM-CRF and CRF machine learning algorithms in NER and the attention-Bi-LSTM and SVM (support vector machines) algorithms in relation recognition.Conclusion In this study, we developed a deep learning approach that fine-tuned BERT to extract the breast cancer concepts and their attributes. It demonstrated its superior performance compared to traditional machine learning algorithms, thus supporting its uses in broader NER and relation extraction tasks in the medical domain.
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The majority of useful clinical data are stored as unstructured free text that requires manual extraction into meaningful clinical features.
“However, the majority of useful clinical data are stored as unstructured free text that requires manual extraction into meaningful clinical features”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [1]
Clinicians spend more time on administrative work reviewing EHRs instead of practising patient-facing medicine.
“therefore, clinicians spend more time on administrative work reviewing EHRs instead of practising patient-facing medicine [1].”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [1]
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- Authors
- Zhang, X., Zhang, Y., Zhang, Q., Ren, Y., Qiu, T., Ma, J., & Sun, Q.
- Affiliations
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Digital China Health (China) (CN)
- Corresponding author
- Ma, Jianhui
- Sun, Qiang
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 132
- Pages
- 103985
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W2978511610
- Cited by
- 160
- References
- 28
- Citations by year
- 2026: 6 · 2025: 20 · 2024: 36 · 2023: 33 · 2022: 30 · 2021: 22 · 2020: 12 · 2019: 1
- FWCI
- 5.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Science and Technology of the People's Republic of China
- Chinese Academy of Medical Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- breast cancer, clinical information extraction, named entity recognition, relation extraction, BERT fine-tuning, clinical text, clinical language models, BiLSTM-CRF, clinical notes
- MeSH terms
- Deep Learning, Algorithms, Breast Neoplasms, China, Female, Humans, Natural Language Processing, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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