The primary objective of this review is to investigate the effectiveness of machine learning and deep learning methodologies in the context of extracting adverse drug events (ADEs) from clinical benchmark datasets. We conduct an in-depth analysis, aiming to compare the merits and drawbacks of both machine learning and deep learning techniques, particularly within the framework of named-entity recognition (NER) and relation classification (RC) tasks related to ADE extraction. Additionally, our focus extends to the examination of specific features and their impact on the overall performance of these methodologies. In a broader perspective, our research extends to ADE extraction from various sources, including biomedical literature, social media data, and drug labels, removing the limitation to exclusively machine learning or deep learning methods. We conducted an extensive literature review on PubMed using the query “(((machine learning [Medical Subject Headings (MeSH) Terms]) OR (deep learning [MeSH Terms])) AND (adverse drug event [MeSH Terms])) AND (extraction)”, and supplemented this with a snowballing approach to review 275 references sourced from retrieved articles. In our analysis, we included twelve articles for review. For the NER task, deep learning models outperformed machine learning models. In the RC task, gradient Boosting, multilayer perceptron and random forest models excelled. The Bidirectional Encoder Representations from Transformers (BERT) model consistently achieved the best performance in the end-to-end task. Future efforts in the end-to-end task should prioritize improving NER accuracy, especially for 'ADE' and 'Reason'. These findings hold significant implications for advancing the field of ADE extraction and pharmacovigilance, ultimately contributing to improved drug safety monitoring and healthcare outcomes.

中文翻译：

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人工智能驱动的药物警戒：对基准数据集的基于临床文本的不良药物事件检测中的机器和深度学习的回顾

本次综述的主要目的是调查机器学习和深度学习方法在从临床基准数据集中提取药物不良事件 (ADE) 的有效性。我们进行了深入分析，旨在比较机器学习和深度学习技术的优缺点，特别是在与 ADE 提取相关的命名实体识别 (NER) 和关系分类 (RC) 任务的框架内。此外，我们的重点还包括检查特定功能及其对这些方法的整体性能的影响。从更广泛的角度来看，我们的研究扩展到从各种来源（包括生物医学文献、社交媒体数据和药物标签）提取 ADE，消除了机器学习或深度学习方法的限制。我们使用查询“(((机器学习 [医学主题词 (MeSH) 术语]) OR (深度学习 [MeSH 术语])) AND (不良药物事件 [MeSH 术语])) AND (提取）”，并通过滚雪球的方法来补充这一点，以审查来自检索到的文章的 275 篇参考文献。在我们的分析中，我们纳入了十二篇文章进行审查。对于 NER 任务，深度学习模型的表现优于机器学习模型。在 RC 任务中，梯度提升、多层感知器和随机森林模型表现出色。 Transformers 的双向编码器表示 (BERT) 模型在端到端任务中始终取得最佳性能。未来在端到端任务中的努力应优先考虑提高 NER 准确性，尤其是“ADE”和“Reason”。这些发现对于推进 ADE 提取和药物警戒领域具有重要意义，最终有助于改善药物安全监测和医疗保健结果。