Beyond Reconstruction: Generating Privacy-Preserving Clinical Letters

Abstract

Due to the sensitive nature of clinical letters, their use in model training, medical research, and education is limited.This work aims to generate diverse, de-identified, and high-quality synthetic clinical letters to enhance privacy protection.This study explores various pretrained language models (PLMs) for text masking and generation, employing various masking strategies with a focus on Bio_ClinicalBERT.Both qualitative and quantitative methods are used for evaluation, supplemented by a downstream Named Entity Recognition (NER) task.Our results indicate that encoder-only models outperform encoder-decoder models.Generaldomain and clinical-domain PLMs exhibit comparable performance when clinical information is preserved.Preserving clinical entities and document structure yields better performance than fine-tuning alone.Masking stopwords enhances text quality, whereas masking nouns or verbs has a negative impact.BERTScore proves to be the most reliable quantitative evaluation metric in our task.Contextual information has minimal impact, indicating that synthetic letters can effectively replace original ones in downstream tasks.Unlike previous studies that focus primarily on reconstructing original letters or training a privacydetection and substitution model, this project provides a framework for generating diverse clinical letters while embedding privacy detection, enabling sensitive dataset expansion and facilitating the use of real-world clinical data.

Authors 5

1. Libo Ren

   University of Manchester

   Affiliation as printed

   The University of Manchester , UK

2. Samuel Belkadi

   University of Cambridge

   Affiliation as printed

   Cambridge University , UK

3. Lifeng Han Aachen LIACS & LUMC

   Leiden University · Leiden University Medical Center · University of Manchester

   Affiliation as printed

   LIACS & LUMC , Leiden University , NL

   The University of Manchester , UK

4. Warren Del-Pinto

   University of Manchester

   Affiliation as printed

   The University of Manchester , UK

5. Goran Nenadić

   University of Manchester

   Affiliation as printed

   The University of Manchester , UK