Analyzing adverse drug events (ADEs) is an integral part of drug safety monitoring, which plays a significant role in medication decision-making. The increasing prevalence of health-related social media may provide an avenue for drug safety profiling using patients' online posts. Recent advances in machine learning, especially in deep learning, have dramatically benefited ADE detection and extraction. However, despite the wide use of state-of-the-art deep learning models, prior research has predominantly analyzed each post independently instead of treating the relevant posts holistically. In this study, guided by the theories of transfer of learning, we adopted the design science research methodology and developed a deep learning-based approach by innovatively integrating historical profiles for ADE detection and extraction. Our framework—the Historical Awareness Multi-Level Embedding (HAMLE) model—outperformed existing state-of-the-art benchmarks by large margins. We also validated its real-world safety monitoring application using the Food and Drug Administration's drug safety warnings, and it showed promising performance in identifying previously unknown ADEs. This confirmed its potential for use as an early warning system for postmarketing surveillance. Furthermore, to evaluate the generalizability of HAMLE, we further tested it on a formal medical dataset extracted from PubMed, and it demonstrated robustness and achieved new state-of-the-art results. Our strategy of building a historical awareness deep learning model, inspired by extant theories, could create a new way to integrate historical profiles and characteristics to support various business tasks in different domains.

分析药物不良事件 (ADEs) 是药物安全监测的一个组成部分，在药物决策中发挥着重要作用。与健康相关的社交媒体的日益流行可能为使用患者在线帖子进行药物安全性分析提供途径。机器学习的最新进展，尤其是深度学习，极大地促进了 ADE 检测和提取。然而，尽管广泛使用最先进的深度学习模型，但先前的研究主要是独立分析每个帖子，而不是整体处理相关帖子。在这项研究中，在学习迁移理论的指导下，我们采用了设计科学研究方法，并通过创新地整合用于 ADE 检测和提取的历史概况，开发了一种基于深度学习的方法。我们的框架——历史A意识多层次E _ _ _mbedding (HAMLE) 模型——大大优于现有的最先进的基准。我们还使用美国食品和药物管理局的药物安全警告验证了其真实世界的安全监测应用程序，并且它在识别以前未知的 ADE 方面表现出良好的性能。这证实了其作为上市后监测预警系统的潜力。此外，为了评估 HAMLE 的普遍性，我们在从 PubMed 提取的正式医学数据集上进一步对其进行了测试，结果证明了它的稳健性并取得了新的最先进的结果。我们在现有理论的启发下构建历史意识深度学习模型的策略可以创造一种整合历史概况和特征的新方法，以支持不同领域的各种业务任务。