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Cited by 160Biomedical Text Mining and OntologiesMachine Learning in HealthcareTopic Modeling

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Extracting comprehensive clinical information for breast cancer using deep learning methods

Zhang, Xiaohui, Zhang, Yaoyun, Zhang, Qin, Ren, Yuankai, Qiu, Tinglin, Ma, Jianhui, Sun, Qiang·2019·International Journal of Medical Informatics
DOI 10.1016/j.ijmedinf.2019.103985
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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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Crossref · 10.1016/j.ijmedinf.2019.103985

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Metadata

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
ORCID
Venue
International Journal of Medical Informatics
Publisher
Elsevier BV
Published
2019-12
Volume
132
Pages
103985
ISSN
1386-5056 (ISSN-L)
DOI
10.1016/j.ijmedinf.2019.103985
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
Licence
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
  • Health Sciences › Medicine › Health Informatics › 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

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