Extracting structured data from unstructured breast imaging reports with transformer-based models.

ABSTRACT:

Background and objective

Structured clinical data is essential for research and informed decision-making, yet medical reports are frequently stored as unstructured free text. This study compared the performance of BERT-based and generative language models in converting unstructured breast imaging reports into structured, tabular data suitable for clinical and research applications.

Methods

A dataset of 286 anonymised breast imaging reports in Spanish was translated into English and used to evaluate five transformer-based models pre-trained in medical data: BlueBERT, BioBERT, BioMedBERT, BioGPT and ClinicalT5. Two natural language processing approaches were explored: classification of 19 categorical variables (e.g. diagnostic technique, report type, family history, BI-RADS ca

SUBMITTER: Carrilero-Mardones M 

PROVIDER: S-EPMC12827707 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature