Critical assessment of transformer-based AI models for German clinical notes.

ABSTRACT:

Objective

Healthcare data such as clinical notes are primarily recorded in an unstructured manner. If adequately translated into structured data, they can be utilized for health economics and set the groundwork for better individualized patient care. To structure clinical notes, deep-learning methods, particularly transformer-based models like Bidirectional Encoder Representations from Transformers (BERT), have recently received much attention. Currently, biomedical applications are primarily focused on the English language. While general-purpose German-language models such as GermanBERT and GottBERT have been published, adaptations for biomedical data are unavailable. This study evaluated the suitability of existing and novel transformer-based models for the German biomedic

SUBMITTER: Lentzen M 

PROVIDER: S-EPMC9663939 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature