The publication MedLinkDE — MedDRA Entity Linking for German with Guided Chain of Thought Reasoning was accepted at the EMNLP 2025.
November, 2025.
MedLinkDE – MedDRA Entity Linking for German with
Guided Chain of Thought Reasoning. Proceedings of the 2025 Conference on Empirical Methods in Natural
Language Processing, 31569–31581.
BibTeX
@inproceedings{Christof:et:al:2025,
author = {Christof, Roman and Zeidi, Farnaz and Messelhäußer, Manuela and Mentzer, Dirk
and Koenig, Renate and Childs, Liam and Mehler, Alexander},
title = {{M}ed{L}ink{DE} {--} {M}ed{DRA} Entity Linking for {G}erman with
Guided Chain of Thought Reasoning},
editor = {Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn
and Peng, Violet},
booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural
Language Processing},
month = {nov},
year = {2025},
address = {Suzhou, China},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2025.emnlp-main.1609/},
doi = {10.18653/v1/2025.emnlp-main.1609},
pages = {31569--31581},
isbn = {979-8-89176-332-6},
pdf = {https://aclanthology.org/2025.emnlp-main.1609.pdf},
abstract = {In pharmacovigilance, effective automation of medical data structuring,
especially linking entities to standardized terminologies such
as MedDRA, is critical. This challenge is rarely addressed for
German data. With MedLinkDE we address German MedDRA entity linking
for adverse drug reactions in a two-step approach: (1) retrieval
of medical terms with fine-tuned embedding models, followed (2)
by guided chain-of-thought re-ranking using LLMs. To this end,
we introduce RENOde, a German real-world MedDRA dataset consisting
of reportings from patients and healthcare professionals. To overcome
the challenges posed by the linguistic diversity of these reports,
we generate synthetic data mapping the two reporting styles of
patients and healthcare professionals. Our embedding models, fine-tuned
on these synthetic, quasi-personalized datasets, show competitive
performance with real datasets in terms of accuracy at high top-
recall, providing a robust basis for re-ranking. Our subsequent
guided Chain of Thought (CoT) re-ranking, informed by MedDRA coding
guidelines, improves entity linking accuracy by approximately
15{\%} (Acc@1) compared to embedding-only strategies. In this
way, our approach demonstrates the feasibility of entity linking
in medical reports under the constraints of data scarcity by relying
on synthetic data reflecting different informant roles of reporting
persons.}
}