@inproceedings{naskar-etal-2025-hybrid,
    title = "A Hybrid System for Comprehensive and Consistent Automated {M}ed{DRA} Coding of Adverse Drug Event",
    author = "Naskar, Abir  and
      Chen, Liuliu  and
      Kang, Jemina  and
      Conway, Mike",
    editor = "Kummerfeld, Jonathan K.  and
      Joshi, Aditya  and
      Dras, Mark",
    booktitle = "Proceedings of The 23rd Annual Workshop of the Australasian Language Technology Association",
    month = nov,
    year = "2025",
    address = "Sydney, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-alta/2025.alta-main.16/",
    pages = "216--223",
    ISBN = "1834-7037",
    abstract = "Normalization of Adverse Drug Events (ADEs), or linking adverse event mentions to standardized dictionary terms, is crucial for harmonizing diverse clinical and patient-reported descriptions, enabling reliable aggregation, accurate signal detection, and effective pharmacovigilance across heterogeneous data sources. The ALTA 2025 shared task focuses on mapping extracted ADEs from documents to a standardized list of MedDRA phrases. This paper presents a system that combines rulebased methods, zero-shot and fine-tuned large language models (LLMs), along with promptbased approaches using the latest commercial LLMs to address this task. Our final system achieves an Accuracy@1 score of 0.3494, ranking second on the shared task leaderboard."
}