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网络论坛中巴氯芬的信号检测:一项初步研究。

Signal Detection for Baclofen in Web Forums: A Preliminary Study.

作者信息

Karapetiantz Pierre, Audeh Bissan, Lillo-Le Louët Agnès, Bousquet Cedric

机构信息

Sorbonne Université, Inserm, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, F-75006 Paris, France.

Centre régional de pharmacovigilance HEGP, AP-HP Paris, France.

出版信息

Stud Health Technol Inform. 2018;247:421-425.

Abstract

Web forums are proposed as a new complementary source of knowledge to spontaneous reports by patients and healthcare professionals due to underreporting of adverse drug reactions (ADRs). Some authors suggest that signal detection could be a convenient method for gathering mentions of ADRs in patients' posts. Signal detection methods were proposed to mine pharmacovigilance databases, but little is known about their applicability to web forums. We describe a method implementing several traditional decision rules on signal detection with baclofen applied to a set of more than 6 million posts. We then cross-validated four unexpected signals applying a logistic regression method. Most adverse effects (AEs) described in the summary of product characteristics of baclofen were detected by signal detection methods. Some unexpected AEs were too. Therefore, web forums are confirmed as a complementary resource for improving current knowledge in pharmacovigilance by detecting unexpected adverse drug reactions.

摘要

由于药物不良反应(ADR)报告不足,网络论坛被提议作为患者和医疗专业人员自发报告之外的一种新的补充知识来源。一些作者认为信号检测可能是一种方便的方法,用于收集患者帖子中有关ADR的提及。信号检测方法被用于挖掘药物警戒数据库,但对于它们在网络论坛中的适用性知之甚少。我们描述了一种方法,该方法对巴氯芬应用信号检测实施了几个传统决策规则,并应用于一组超过600万个帖子。然后,我们使用逻辑回归方法对四个意外信号进行了交叉验证。巴氯芬产品特性摘要中描述的大多数不良反应(AE)都通过信号检测方法检测到了。一些意外的AE也是如此。因此,通过检测意外的药物不良反应,网络论坛被确认为一种补充资源,可用于改善当前药物警戒方面的知识。

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