Yu Deahan, Vydiswaran V G Vinod
School of Information, University of Michigan, Ann Arbor, MI, United States.
Department of Learning Health Sciences, Medical School, University of Michigan, Ann Arbor, MI, United States.
JMIR Med Inform. 2022 Sep 28;10(9):e38140. doi: 10.2196/38140.
Adverse reactions to drugs attract significant concern in both clinical practice and public health monitoring. Multiple measures have been put into place to increase postmarketing surveillance of the adverse effects of drugs and to improve drug safety. These measures include implementing spontaneous reporting systems and developing automated natural language processing systems based on data from electronic health records and social media to collect evidence of adverse drug events that can be further investigated as possible adverse reactions.
While using social media for collecting evidence of adverse drug events has potential, it is not clear whether social media are a reliable source for this information. Our work aims to (1) develop natural language processing approaches to identify adverse drug events on social media and (2) assess the reliability of social media data to identify adverse drug events.
We propose a collocated long short-term memory network model with attentive pooling and aggregated, contextual representation generated by a pretrained model. We applied this model on large-scale Twitter data to identify adverse drug event-related tweets. We conducted a qualitative content analysis of these tweets to validate the reliability of social media data as a means to collect such information.
The model outperformed a variant without contextual representation during both the validation and evaluation phases. Through the content analysis of adverse drug event tweets, we observed that adverse drug event-related discussions had 7 themes. Mental health-related, sleep-related, and pain-related adverse drug event discussions were most frequent. We also contrast known adverse drug reactions to those mentioned in tweets.
We observed a distinct improvement in the model when it used contextual information. However, our results reveal weak generalizability of the current systems to unseen data. Additional research is needed to fully utilize social media data and improve the robustness and reliability of natural language processing systems. The content analysis, on the other hand, showed that Twitter covered a sufficiently wide range of adverse drug events, as well as known adverse reactions, for the drugs mentioned in tweets. Our work demonstrates that social media can be a reliable data source for collecting adverse drug event mentions.
药物不良反应在临床实践和公共卫生监测中都备受关注。已采取多项措施来加强药物上市后不良反应监测并提高药物安全性。这些措施包括实施自发报告系统，以及基于电子健康记录和社交媒体数据开发自动化自然语言处理系统，以收集可作为可能不良反应进一步调查的药物不良事件证据。
虽然利用社交媒体收集药物不良事件证据具有潜力，但尚不清楚社交媒体是否是获取此类信息的可靠来源。我们的工作旨在：（1）开发自然语言处理方法以识别社交媒体上的药物不良事件；（2）评估社交媒体数据识别药物不良事件的可靠性。
我们提出一种带有注意力池化的并置长短期记忆网络模型，以及由预训练模型生成的聚合上下文表示。我们将此模型应用于大规模推特数据，以识别与药物不良事件相关的推文。我们对这些推文进行了定性内容分析，以验证社交媒体数据作为收集此类信息手段的可靠性。
在验证和评估阶段，该模型均优于无上下文表示的变体。通过对药物不良事件推文的内容分析，我们观察到与药物不良事件相关的讨论有7个主题。与心理健康、睡眠和疼痛相关的药物不良事件讨论最为频繁。我们还将已知的药物不良反应与推文中提到的进行了对比。
我们观察到该模型在使用上下文信息时明显改进。然而，我们的结果显示当前系统对未见数据的泛化能力较弱。需要进一步研究以充分利用社交媒体数据并提高自然语言处理系统的稳健性和可靠性。另一方面，内容分析表明，推特涵盖了足够广泛的药物不良事件以及推文中提到药物的已知不良反应。我们的工作表明社交媒体可以是收集药物不良事件提及信息的可靠数据源。
