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

ABSTRACT: 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

SUBMITTER: Kwok COT 

PROVIDER: S-EPMC12615701 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature