A Transformer-Based Pipeline for German Clinical Document De-Identification.

ABSTRACT:

Objective

 Commercially available large language models such as Chat Generative Pre-Trained Transformer (ChatGPT) cannot be applied to real patient data for data protection reasons. At the same time, de-identification of clinical unstructured data is a tedious and time-consuming task when done manually. Since transformer models can efficiently process and analyze large amounts of text data, our study aims to explore the impact of a large training dataset on the performance of this task.

Methods

 We utilized a substantial dataset of 10,240 German hospital documents from 1,130 patients, created as part of the investigating hospital's routine documentation, as training data. Our approach involved fine-tuning and training an ensemble of two transformer-based language models simu

SUBMITTER: Arzideh K 

PROVIDER: S-EPMC11710903 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature