Uncovering Topics in Dutch Patient Messages in Inflammatory Bowel Disease: A Comparative Study of Embedding Models for Neural Topic Modeling.

This study applied natural language processing to identify common topics in 12,054 Dutch patient-provider messages in inflammatory bowel disease. Using the BERTopic framework, three embedding models were evaluated with topic diversity, Cv coherence, and topic assignment proportion. A lightweight Dutch-specific embedding model (RobBERT) outperformed two larger multilingual models (MPNet Sentence Transformer and QWEN3-embedding-8B). We identified 120 sentence-level topics covering medical and administrative themes. Results showed that 22% of messages did not require specialist attention, highlighting the potential of neural topic models for automated triage in digital IBD care.