a Unit of Pharmacology and Pharmacovigilance , University of Pisa , Pisa , Italy.
b Division of Pharmacology and Pharmacovigilance, Department of Clinical and Experimental Medicine , University Hospital of Pisa , Pisa , Italy.
Expert Opin Drug Saf. 2018 Nov;17(11):1081-1093. doi: 10.1080/14740338.2018.1531847. Epub 2018 Oct 12.
Social media mining could be a possible strategy to retrieve drug safety information. The mining of social media is a complex process under progressive evolution, falling into three broad categories: listening (safety data reporting), engaging (follow-up), and broadcasting (risk communication). This systematic review is aimed at evaluating the usefulness and quality of proto-signals by social media listening. Areas covered: In this systematic search, performed according to MOOSE and PRISMA statements, we selected studies, published in MEDLINE, EMBASE, and Google Scholar until 31 December 2017, that listened at least one social media to identify proto-adverse drug events and proto-signals. Expert opinion: The selected 38 studies identified serious and unexpected proto-adverse drug events characterized by poorer information quality as compared with spontaneous reporting databases. This feature allows rarely the evaluation of causal relationships. Proto-signals identified by social media listening had the potential of anticipating pre-specified known signals in only six studies. Moreover, the personal perception of patients reported in social media could be used to implement effective risk communication strategies. However, signal detection in social media cannot be currently recommended for routine pharmacovigilance, due to logistic and technical issues.
社交媒体挖掘可能是检索药物安全信息的一种可行策略。社交媒体挖掘是一个不断发展的复杂过程，可分为三大类：监听（安全数据报告）、参与（跟进）和广播（风险沟通）。本系统评价旨在评估社交媒体监听的原始信号的有用性和质量。涵盖领域：在根据 MOOSE 和 PRISMA 声明进行的系统检索中，我们选择了至少使用一种社交媒体来识别原始药物不良事件和原始信号的研究，这些研究发表在 MEDLINE、EMBASE 和 Google Scholar 上，截至 2017 年 12 月 31 日。专家意见：与自发报告数据库相比，所选的 38 项研究确定了严重和意外的原始药物不良事件，其信息质量较差。这一特征使得评估因果关系变得很困难。仅在 6 项研究中，通过社交媒体监听识别出的原始信号有可能预先确定已知信号。此外，在社交媒体中报告的患者的个人看法可用于实施有效的风险沟通策略。然而，由于后勤和技术问题，目前不能推荐在常规药物警戒中使用社交媒体进行信号检测。
