@inproceedings{christof-etal-2025-medlinkde,
    title = "{M}ed{L}ink{DE} {--} {M}ed{DRA} Entity Linking for {G}erman with Guided Chain of Thought Reasoning",
    author = {Christof, Roman  and
      Zeidi, Farnaz  and
      Messelh{\"a}u{\ss}er, Manuela  and
      Mentzer, Dirk  and
      Koenig, Renate  and
      Childs, Liam  and
      Mehler, Alexander},
    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://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.1609/",
    doi = "10.18653/v1/2025.emnlp-main.1609",
    pages = "31569--31581",
    ISBN = "979-8-89176-332-6",
    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."
}