Digital drug safety surveillance: monitoring pharmaceutical products in twitter.
Digital drug safety surveillance: monitoring pharmaceutical products in twitter.
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DOI：
10.1007/s40264-014-0155-x
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发表时间：
2014-05
期刊：
影响因子：
4.2
通讯作者：
Dasgupta, Nabarun
中科院分区：
文献类型：
--
作者：
Freifeld, Clark C.;Brownstein, John S.;Menone, Christopher M.;Bao, Wenjie;Filice, Ross;Kass-Hout, Taha;Dasgupta, Nabarun
传统的不良事件(AE)报告系统在适应患者在线不良事件报告方面一直进展缓慢，转而依赖临床医生和药物安全组织等看门人来验证每个潜在事件。与此同时，越来越多的患者转向社交媒体，分享他们使用药物、医疗器械和疫苗的经验。这项研究的目的是评估推特上提到类似AE反应的帖子与监管机构收到的自发报告之间的一致性水平。我们收集了2012年11月1日至2013年5月31日期间提及23种医疗产品的公开英文推文。使用半自动过程对数据进行过滤，以确定与原始环境相似的帖子。开发了一本词典，用于将互联网白话翻译成标准化的法规本体进行分析(MedDRA®)。然后将确定的产品-事件对的聚合频率与公共FDA不良事件报告系统(FAERS)的数据按系统器官类别(SOC)进行比较。在收集的690万条推特帖子中，在6万条检查中确定了4401条原始AEs。基于词典的自动症状分类具有72%的召回率和86%的准确率。观察到了类似的总体分布情况，推特上报告的原始AEs和SOC报告的FAERS之间的Spearman等级相关Rho为0.75%(P<；0.0001)。在Twitter上报告AEs的患者在描述他们的经历时表现出了一系列的老练。尽管这些数据已公之于众，但它们在药物警戒中的适当作用尚未确定。还需要做更多的工作来改进数据获取和自动化。
Traditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients, relying instead on gatekeepers, such as clinicians and drug safety groups, to verify each potential event. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines. The aim of the study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency. We collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA®). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC). Of the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 72 % recall and 86 % precision. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 (p < 0.0001) between Proto-AEs reported in Twitter and FAERS by SOC. Patients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation.
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影响因子：
4.2
作者：
Friedman, Richard J.;Hess, Susanne;Homering, Martin
通讯作者：
Homering, Martin
影响因子：
2.6
作者：
Duggirala, Hesha J.;Herz, Naomi D.;Marinac-Dabic, Danica
通讯作者：
Marinac-Dabic, Danica
影响因子：
--
作者：
Moore, Thomas J.;Cohen, Michael R.;Furberg, Curt D.
通讯作者：
Furberg, Curt D.
DOI：
10.1136/amiajnl-2012-001482
发表时间：
2013-05-01
影响因子：
6.4
作者：
White, Ryen W.;Tatonetti, Nicholas P.;Horvitz, Eric
通讯作者：
Horvitz, Eric
影响因子：
6.4
作者：
Freifeld, Clark C.;Mandl, Kenneth D.;Ras, Ben Y.;Bronwnstein, John S.
通讯作者：
Bronwnstein, John S.