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        <title>A Hybrid System for Comprehensive and Consistent Automated MedDRA Coding of Adverse Drug Event</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Hybrid System for Comprehensive and Consistent Automated MedDRA Coding of Adverse Drug Event
%A Naskar, Abir
%A Chen, Liuliu
%A Kang, Jemina
%A Conway, Mike
%Y Kummerfeld, Jonathan K.
%Y Joshi, Aditya
%Y Dras, Mark
%S Proceedings of the 23rd Annual Workshop of the Australasian Language Technology Association
%D 2025
%8 November
%I Association for Computational Linguistics
%C Sydney, Australia
%@ 1834-7037
%F naskar-etal-2025-hybrid
%X 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.
%U https://aclanthology.org/2025.alta-main.16/
%P 216-223

Markdown (Informal)

[A Hybrid System for Comprehensive and Consistent Automated MedDRA Coding of Adverse Drug Event](https://aclanthology.org/2025.alta-main.16/) (Naskar et al., ALTA 2025)

• A Hybrid System for Comprehensive and Consistent Automated MedDRA Coding of Adverse Drug Event (Naskar et al., ALTA 2025)

ACL

• Abir Naskar, Liuliu Chen, Jemina Kang, and Mike Conway. 2025. A Hybrid System for Comprehensive and Consistent Automated MedDRA Coding of Adverse Drug Event. In Proceedings of the 23rd Annual Workshop of the Australasian Language Technology Association, pages 216–223, Sydney, Australia. Association for Computational Linguistics.