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        <title>LHS712_ADENotGood at #SMM4H 2024 Task 1: Deep-LLMADEminer: A deep learning and LLM pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter</title>
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    <abstract>Adverse drug events (ADEs) pose major public health risks, with traditional reporting systems often failing to capture them. Our proposed pipeline, called Deep-LLMADEminer, used natural language processing approaches to tackle this issue for #SMM4H 2024 shared task 1. Using annotated tweets, we built a three part pipeline: RoBERTa for classification, GPT-4-turbo for span extraction, and BioBERT for normalization. Our models achieved F1-scores of 0.838, 0.306, and 0.354, respectively, offering a novel system for Task 1 and similar pharmacovigilance tasks.</abstract>
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%0 Conference Proceedings
%T LHS712_ADENotGood at #SMM4H 2024 Task 1: Deep-LLMADEminer: A deep learning and LLM pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter
%A Zheng, Yifan
%A Gong, Jun
%A Ren, Shushun
%A Simancek, Dalton
%A Vydiswaran, V.G.Vinod
%Y Xu, Dongfang
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zheng-etal-2024-lhs712
%X Adverse drug events (ADEs) pose major public health risks, with traditional reporting systems often failing to capture them. Our proposed pipeline, called Deep-LLMADEminer, used natural language processing approaches to tackle this issue for #SMM4H 2024 shared task 1. Using annotated tweets, we built a three part pipeline: RoBERTa for classification, GPT-4-turbo for span extraction, and BioBERT for normalization. Our models achieved F1-scores of 0.838, 0.306, and 0.354, respectively, offering a novel system for Task 1 and similar pharmacovigilance tasks.
%U https://aclanthology.org/2024.smm4h-1.30
%P 130-132

Markdown (Informal)

[LHS712_ADENotGood at #SMM4H 2024 Task 1: Deep-LLMADEminer: A deep learning and LLM pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter](https://aclanthology.org/2024.smm4h-1.30) (Zheng et al., SMM4H-WS 2024)

• LHS712_ADENotGood at #SMM4H 2024 Task 1: Deep-LLMADEminer: A deep learning and LLM pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter (Zheng et al., SMM4H-WS 2024)

ACL

• Yifan Zheng, Jun Gong, Shushun Ren, Dalton Simancek, and V.G.Vinod Vydiswaran. 2024. LHS712_ADENotGood at #SMM4H 2024 Task 1: Deep-LLMADEminer: A deep learning and LLM pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 130–132, Bangkok, Thailand. Association for Computational Linguistics.