Advancements in Multilingual Biomedical Natural Language Processing: exploring Large Language Models for Named Entity Recognition and Linking
Journal:
medRxiv
Published Date:
Jan 23, 2026
Abstract
Linking (BEL) are essential for transforming unstructured Electronic Health Records (EHRs) into structured information. However, tools for these tasks are limited in non-English biomedical texts such as Dutch and Italian. This study investigates the use of prompt-based learning with Large Language Models (LLMs) to perform multilingual NER and BEL using minimal domainspecific data, while addressing annotation preservation during corpus translation. Methods: An English-annotated corpus from the ShARe/CLEF dataset was translated into Dutch and Italian using a strategy that embeds annota-tions directly into the text prior to translation and retrieves them afterwards. GPT-4o was applied in zero-shot and few-shot settings to extract biomedical entities, which were then mapped to Unified Medical Language System Concept Unique Identifiers using contextual word embeddings. Performance was evaluated with precision, recall, and F1-score, and compared with goldstandard clinician annotations. Results: The multilingual NER pipeline achieved strong performance, with an overall F1-score of 0.98 across languages. BEL experiments showed reliable entity normalization, with an overall accuracy of 0.91 and a mean reciprocal rank of 0.95. The combined performance of the NER and BEL achieved 0.90 supporting the utility of LLMs in standardizing biomedical concepts across languages. Conclusion: Prompt-based LLMs can effectively perform NER and BEL in languages with less annotated resources, even with limited annotated training data. The proposed annotation-preserving translation method, combined with generative and discriminative LLM capabilities, provides a scalable approach to multilingual clinical information extraction. These findings highlight the potential for broader adoption of LLM-based natural language processing systems to support multilingual healthcare data harmonization.
