Work

German Medical NER with BERT and LLMs: The Impact of Training Data Size

Year: 2025

Type: book-chapter

Abstract: Named Entity Recognition (NER) in the medical domain often presents significant challenges due to the complexity and specificity of medical terminology, especially in lower-resource settings where annotated data is scarce. This study explores the performance of an exemplary large language model and a BERT-based model in the context of NER for German medical texts. We focus on the impact of different data sizes for training and their performance to simulate lower-resource conditions. Both models are evaluated on two German annotated corpora. Our results reveal that both models perform rather similar on both datasets, with LLaMA3.1 performing slightly better with less training material.

Source: Studies in health technology and informatics

DOI: 10.3233/shti250469

Authors: Suteera Seeha, Sihan Wu, Justin Hofenbitzer, Claudio Benzoni, Peter Pallaoro, Raphael Scheible, Martin Boeker, Luise Modersohn

Institutions: TUM Klinikum, Technical University of Munich

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

OpenAlex ID: W4410447908

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