| アブストラクト | OBJECTIVE: Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional deep learning models are adept at learning intricate feature representations and dependencies in sequential data, but often require extensive labeled data. In contrast, large language models (LLMs) excel in understanding contextual information, but exhibit unstable performance on named entity recognition (NER) tasks, possibly due to their broad but unspecific training. This study aims to evaluate the effectiveness of LLMs and traditional deep learning models in AE extraction, and to assess the impact of ensembling these models on performance. METHODS: In this study, we utilized reports and posts from the Vaccine Adverse Event Reporting System (VAERS) (n = 230), Twitter (n = 3,383), and Reddit (n = 49) as our corpora. Our goal was to extract three types of entities: vaccine, shot, and adverse event (ae). We explored and fine-tuned (except GPT-4) multiple LLMs, including GPT-2, GPT-3.5, GPT-4, Llama-2 7b, and Llama-2 13b, as well as traditional deep learning models like Recurrent neural network (RNN) and Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT). To enhance performance, we created ensembles of the three models with the best performance. For evaluation, we used strict and relaxed F1 scores to evaluate the performance for each entity type, and micro-average F1 was used to assess the overall performance. RESULTS: The ensemble demonstrated the best performance in identifying the entities "vaccine," "shot," and "ae," achieving strict F1-scores of 0.878, 0.930, and 0.925, respectively, and a micro-average score of 0.903. These results underscore the significance of fine-tuning models for specific tasks and demonstrate the effectiveness of ensemble methods in enhancing performance. CONCLUSION: In conclusion, this study demonstrates the effectiveness and robustness of ensembling fine-tuned traditional deep learning models and LLMs, for extracting AE-related information following COVID-19 vaccination. This study contributes to the advancement of natural language processing in the biomedical domain, providing valuable insights into improving AE extraction from text data for pharmacovigilance and public health surveillance. |
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| ジャーナル名 | Journal of biomedical informatics |
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| Pubmed追加日 | 2025/2/10 |
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| 投稿者 | Li, Yiming; Viswaroopan, Deepthi; He, William; Li, Jianfu; Zuo, Xu; Xu, Hua; Tao, Cui |
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| 組織名 | McWilliams School of Biomedical Informatics, The University of Texas Health;Science Center at Houston, Houston, TX 77030, USA.;Department of Electrical & Computer Engineering, Pratt School of Engineering,;Duke University, 305 Tower Engineering Building, Durham, NC 27708, USA.;Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville,;FL 32224, USA.;Department of Biomedical Informatics and Data Science, School of Medicine, Yale;University, New Haven, CT 06510, USA.;FL 32224, USA. Electronic address: Tao.Cui@mayo.edu. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/39923968/ |
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