  Biomedical entity linking, a main component in automatic information
extraction from health-related texts, plays a pivotal role in connecting
textual entities (such as diseases, drugs and body parts mentioned by patients)
to their corresponding concepts in a structured biomedical knowledge base. The
task remains challenging despite recent developments in natural language
processing. This paper presents the first evaluated biomedical entity linking
model for the Dutch language. We use MedRoBERTa.nl as base model and perform
second-phase pretraining through self-alignment on a Dutch biomedical ontology
extracted from the UMLS and Dutch SNOMED. We derive a corpus from Wikipedia of
ontology-linked Dutch biomedical entities in context and fine-tune our model on
this dataset. We evaluate our model on the Dutch portion of the Mantra
GSC-corpus and achieve 54.7% classification accuracy and 69.8% 1-distance
accuracy. We then perform a case study on a collection of unlabeled,
patient-support forum data and show that our model is hampered by the limited
quality of the preceding entity recognition step. Manual evaluation of small
sample indicates that of the correctly extracted entities, around 65% is linked
to the correct concept in the ontology. Our results indicate that biomedical
entity linking in a language other than English remains challenging, but our
Dutch model can be used to for high-level analysis of patient-generated text.
