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<p>Data extraction from medical records is crucial for clinical research, with current methods relying on human annotation. Natural Language Processing (NLP) and Machine Learning-based approaches show promise. We develop and evaluate an NLP pipeline constructed by selecting among four candidate models; ClinicalBERT, PubMedBERT, BioMedRoBERTa and Mistral-Nemo LLM to automate data extraction of 1,795 breast cancer pathology reports obtained from the Providence Health Services Authority in British Columbia. We also explore the effect of further pre-training the BERT-based models using the SQuAD question-answering dataset. Accuracy was evaluated by comparing model output and human annotation. PubMedBERT pre-trained on SQuAD proved to be the best performing model, achieving an overall accuracy of 97.4%. 30 of the 32 FOIs had an accuracy greater than 95.0%. Our model outperformed a previous rule-based algorithm (95.6%). Our findings demonstrate how a high-performing question-answering NLP pipeline for breast cancer pathology can provide a scalable approach to high-fidelity extraction of clinicopathologic features, thereby enhancing research efficiency and improving clinical outcomes.</p>

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Development of a machine learning model for automatic data extraction from breast cancer pathology reports

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月13日

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  • Christy Oi Ting Kwok,
  • Gregory Arbour,
  • Annah Zhang,
  • Jenna Hiemstra,
  • Alan M. Nichol,
  • Raymond Ng,
  • Kathryn V. Isaac

摘要

Data extraction from medical records is crucial for clinical research, with current methods relying on human annotation. Natural Language Processing (NLP) and Machine Learning-based approaches show promise. We develop and evaluate an NLP pipeline constructed by selecting among four candidate models; ClinicalBERT, PubMedBERT, BioMedRoBERTa and Mistral-Nemo LLM to automate data extraction of 1,795 breast cancer pathology reports obtained from the Providence Health Services Authority in British Columbia. We also explore the effect of further pre-training the BERT-based models using the SQuAD question-answering dataset. Accuracy was evaluated by comparing model output and human annotation. PubMedBERT pre-trained on SQuAD proved to be the best performing model, achieving an overall accuracy of 97.4%. 30 of the 32 FOIs had an accuracy greater than 95.0%. Our model outperformed a previous rule-based algorithm (95.6%). Our findings demonstrate how a high-performing question-answering NLP pipeline for breast cancer pathology can provide a scalable approach to high-fidelity extraction of clinicopathologic features, thereby enhancing research efficiency and improving clinical outcomes.