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Related Experiment Video

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Extracting comprehensive clinical information for breast cancer using deep learning methods.

Xiaohui Zhang1, Yaoyun Zhang2, Qin Zhang2

  • 1Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, China.

International Journal of Medical Informatics
|October 19, 2019
PubMed
Summary

This study introduces a deep learning model using Bidirectional Encoder Representations from Transformers (BERT) to extract comprehensive breast cancer patient information. The model significantly improves named entity recognition (NER) and relation extraction from clinical documents.

Keywords:
Breast cancerClinical information extractionDeep learningFine-tuning BERTInformation model

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Oncology

Background:

  • Breast cancer diagnosis and treatment generate vast amounts of data across various clinical fields.
  • Existing studies often focus on single data types, limiting comprehensive information extraction.
  • A unified approach is needed to model the entire clinical journey of breast cancer patients.

Purpose of the Study:

  • To develop a comprehensive information model for whole-course breast cancer patient data.
  • To apply deep learning, specifically fine-tuned Bidirectional Encoder Representations from Transformers (BERT), for extracting concepts and attributes from clinical breast cancer documents.

Main Methods:

  • A clinical corpus of 100 breast cancer patients' records was utilized.
  • A two-component system was developed: Named Entity Recognition (NER) and Relation Recognition, both fine-tuned using BERT.
  • A pre-trained clinical language model using BERT on Chinese clinical text was employed.

Main Results:

  • The fine-tuned BERT model achieved high performance, with F1 scores of 93.53% for NER and 96.73% for relation extraction.
  • Deep learning approaches significantly outperformed traditional machine learning algorithms like Bi-LSTM-CRF, CRF, attention-Bi-LSTM, and SVM.
  • The system demonstrated superior accuracy in identifying breast cancer concepts and their relationships.

Conclusions:

  • A novel deep learning approach fine-tuning BERT effectively extracts breast cancer concepts and attributes.
  • This method offers superior performance over traditional machine learning for NER and relation extraction in the medical domain.
  • The developed model supports broader applications in medical NLP tasks for enhanced clinical data utilization.