Chee Brant W, Berlin Richard, Schatz Bruce
Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
AMIA Annu Symp Proc. 2011;2011:217-26. Epub 2011 Oct 22.
Adverse drug events (ADEs) remain a large problem in the United States, being the fourth leading cause of death, despite post market drug surveillance. Much post consumer drug surveillance relies on self-reported "spontaneous" patient data. Previous work has performed datamining over the FDA's Adverse Event Reporting System (AERS) and other spontaneous reporting systems to identify drug interactions and drugs correlated with high rates of serious adverse events. However, safety problems have resulted from the lack of post marketing surveillance information about drugs, with underreporting rates of up to 98% within such systems. We explore the use of online health forums as a source of data to identify drugs for further FDA scrutiny. In this work we aggregate individuals' opinions and review of drugs similar to crowd intelligence3. We use natural language processing to group drugs discussed in similar ways and are able to successfully identify drugs withdrawn from the market based on messages discussing them before their removal.
在美国，药物不良事件（ADEs）仍然是一个大问题，尽管有上市后药品监测，但它仍是第四大死因。许多消费者用药监测依赖于患者自我报告的“自发”数据。此前的工作已对美国食品药品监督管理局（FDA）的不良事件报告系统（AERS）及其他自发报告系统进行数据挖掘，以识别药物相互作用以及与高严重不良事件发生率相关的药物。然而，由于缺乏药品上市后监测信息，此类系统的漏报率高达98%，导致了安全问题。我们探索将在线健康论坛作为一种数据来源，以识别需FDA进一步审查的药物。在这项工作中，我们汇总个人对药物的看法和评价，类似于群体智慧。我们使用自然语言处理对以相似方式讨论的药物进行分组，并能够根据在药物退市前讨论它们的信息成功识别已退市的药物。
