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首页> 外文期刊>JMIR public health and surveillance. >Use of social media in detecting drug safety-related new black box warnings, labeling changes, or withdrawals: A scoping review
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Use of social media in detecting drug safety-related new black box warnings, labeling changes, or withdrawals: A scoping review

机译：使用社交媒体在检测药物安全相关的新黑匣子警告中，标记更改或提款：范围审查

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Background Social media has become a new source for obtaining real-world data on adverse drug reactions. Many studies have investigated the use of social media to detect early signals of adverse drug reactions. However, the trustworthiness of signals derived from social media is questionable. To confirm this, a confirmatory study with a positive control (eg, new black box warnings, labeling changes, or withdrawals) is required. Objective This study aimed to evaluate the use of social media in detecting new black box warnings, labeling changes, or withdrawals in advance. Methods This scoping review adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. A researcher searched PubMed and EMBASE in January 2021. Original studies analyzing black box warnings, labeling changes, or withdrawals from social media were selected, and the results of the studies were summarized. Results A total of 14 studies were included in this scoping review. Most studies (8/14, 57.1%%) collected data from a single source, and 10 (71.4%) used specialized health care social networks and forums. The analytical methods used in these studies varied considerably. Three studies (21.4%) manually annotated posts, while 5 (35.7%) adopted machine learning algorithms. Nine studies (64.2%) concluded that social media could detect signals 3 months to 9 years before action from regulatory authorities. Most of these studies (8/9, 88.9%) were conducted on specialized health care social networks and forums. On the contrary, 5 (35.7%) studies yielded modest or negative results. Of these, 2 (40%) used generic social networking sites, 2 (40%) used specialized health care networks and forums, and 1 (20%) used both generic social networking sites and specialized health care social networks and forums. The most recently published study recommends not using social media for pharmacovigilance. Several challenges remain in using social media for pharmacovigilance regarding coverage, data quality, and analytic processing. Conclusions Social media, along with conventional pharmacovigilance measures, can be used to detect signals associated with new black box warnings, labeling changes, or withdrawals. Several challenges remain; however, social media will be useful for signal detection of frequently mentioned drugs in specialized health care social networks and forums. Further studies are required to advance natural language processing and mine real-world data on social media.
机译：背景社交媒体已成为获取对不良药物的真实数据的新来源。许多研究已经研究了使用社交媒体来检测不良药物反应的早期信号。然而，来自社交媒体的信号的可信度是值得怀疑的。为了确认这一点，需要具有积极控制的确认研究（例如，新的黑匣子警告，标签更改或提取）。目的本研究旨在评估社交媒体在检测新的黑匣子警告，标记更改或提前提取的使用。方法审查此范围审查遵守系统评价和Meta分析的首选报告项目，用于划分审查清单。一名研究员在2021年1月搜索了PubMed and Embase.选择了分析黑匣子警告，标签变更或从社交媒体提取的原创研究，总结了研究结果。结果在此范围审查中共有14项研究。大多数研究（8/14,57.1 %%）从单一来源收集数据，10（71.4％）使用专业的医疗社会网络和论坛。这些研究中使用的分析方法变化很大。三项研究（21.4％）手动注释帖子，而5（35.7％）采用机器学习算法。九项研究（64.2％）得出结论，社交媒体可以在监管机构的行动前检测3个月至9年的信号。这些研究中的大多数（8/9,88.9％）于专门的医疗保健社交网络和论坛进行。相反，5（35.7％）的研究产生了适度或负面的结果。其中2（40％）二手通用社交网站，2（40％）二手专业医疗保健网络和论坛，1（20％）使用了通用社交网站和专业的医疗社交网络和论坛。最近发表的研究建议不要使用社交媒体进行药物检测。利用社交媒体对关于覆盖率，数据质量和分析处理的药物媒体，仍有一些挑战。结论社交媒体以及传统的药物理解措施可用于检测与新的黑匣子警告相关的信号，标记变化或取款。仍有几个挑战;然而，社交媒体对于在专业医疗社交网络和论坛中的常见提到的药物的信号检测有用。需要进一步的研究来推进自然语言处理和社交媒体上的现实世界数据。

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