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DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter
Journal of the American Medical Informatics Association ( IF 6.4 ) Pub Date : 2021-07-16 , DOI: 10.1093/jamia/ocab114
Arjun Magge 1 , Elena Tutubalina 2 , Zulfat Miftahutdinov 2 , Ilseyar Alimova 2 , Anne Dirkson 3 , Suzan Verberne 3 , Davy Weissenbacher 1 , Graciela Gonzalez-Hernandez 1
Affiliation  

Abstract
Objective
Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks: ADE classification, named entity recognition for identifying the span of ADE mentions, and ADE mention normalization to standardized terminologies. While the common goal of such systems is to detect ADE signals that can be used to inform public policy, it has been impeded largely by limited end-to-end solutions for large-scale analysis of social media reports for different drugs.
Materials and Methods
We present a dataset for training and evaluation of ADE pipelines where the ADE distribution is closer to the average ‘natural balance’ with ADEs present in about 7% of the tweets. The deep learning architecture involves an ADE extraction pipeline with individual components for all 3 tasks.
Results
The system presented achieved state-of-the-art performance on comparable datasets and scored a classification performance of F1 = 0.63, span extraction performance of F1 = 0.44 and an end-to-end entity resolution performance of F1 = 0.34 on the presented dataset.
Discussion
The performance of the models continues to highlight multiple challenges when deploying pharmacovigilance systems that use social media data. We discuss the implications of such models in the downstream tasks of signal detection and suggest future enhancements.
Conclusion
Mining ADEs from Twitter posts using a pipeline architecture requires the different components to be trained and tuned based on input data imbalance in order to ensure optimal performance on the end-to-end resolution task.


中文翻译:

DeepADEMiner:一种深度学习药物警戒管道,用于提取和规范 Twitter 上提及的不良药物事件

摘要
客观的
来自社交媒体数据的药物警戒研究侧重于使用带注释的数据集挖掘药物不良事件 (ADE),出版物通常侧重于 3 项任务中的一项:ADE 分类、用于识别 ADE 提及范围的命名实体识别以及 ADE 提及归一化到标准化术语。虽然此类系统的共同目标是检测可用于为公共政策提供信息的 ADE 信号,但它在很大程度上受到用于对不同药物的社交媒体报告进行大规模分析的端到端解决方案有限的阻碍。
材料和方法
我们提供了一个用于训练和评估 ADE 管道的数据集,其中 ADE 分布更接近平均“自然平衡”,大约 7% 的推文中存在 ADE。深度学习架构涉及一个 ADE 提取管道,其中包含用于所有 3 个任务的单独组件。
结果
该系统在可比数据集上实现了最先进的性能,并获得了 F 1 = 0.63的分类性能、F 1 = 0.44 的跨度提取性能和 F 1 = 0.34的端到端实体解析性能呈现的数据集。
讨论
在部署使用社交媒体数据的药物警戒系统时,模型的性能继续凸显多重挑战。我们讨论了此类模型在信号检测的下游任务中的影响,并提出了未来的改进建议。
结论
使用管道架构从 Twitter 帖子中挖掘 ADE 需要根据输入数据不平衡对不同的组件进行训练和调整,以确保端到端解析任务的最佳性能。
更新日期:2021-09-20
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