@文章{信息:doi/10.2196/38140，作者="Yu, Deahan and Vydiswaran, V G Vinod"，标题="自然语言处理对社交媒体药物不良事件提及的评估:模型开发与分析"，期刊="JMIR Med Inform"，年="2022"，月="Sep"，日="28"，卷="10"，数="9"，页数="e38140"，关键词="自然语言处理;机器学习;药物不良事件;药物警戒;社交媒体;药物;临床;公共卫生;健康监测;监测; drug effects; drug safety", abstract="Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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. ", issn="2291-9694", doi="10.2196/38140", url="https://medinform.www.mybigtv.com/2022/9/e38140", url="https://doi.org/10.2196/38140", url="http://www.ncbi.nlm.nih.gov/pubmed/36170004" }