Synthetic4Health: generating annotated synthetic clinical letters.

ABSTRACT: Clinical letters contain sensitive information, limiting their use in model training, medical research, and education. This study aims to generate reliable, diverse, and de-identified synthetic clinical letters to support these tasks. We investigated multiple pre-trained language models for text masking and generation, focusing on Bio_ClinicalBERT, and applied different masking strategies. Evaluation included qualitative and quantitative assessments, downstream named entity recognition (NER) tasks, and clinically focused evaluations using BioGPT and GPT-3.5-turbo. The experiments show: (1) encoder-only models perform better than encoder-decoder models; (2) models trained on general corpora perform comparably to clinical-domain models if clinical entities are preserved; (3) preserving clini

SUBMITTER: Ren L 

PROVIDER: S-EPMC12163008 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature