Social Media Mining for Pharmacovigilance

项目摘要 药物在上市前要经过广泛的动物试验和临床试验。 广泛使用。上市前测试产生了关于药物疗效的相当高质量的信息 作为一种治疗它被批准的疾病的方法，但给出了一个非常不完整的图景 药物的安全性。只有在一种药物在更长的时期内被更广泛地销售和使用之后，它才会 有可能确定其他影响的时间，例如罕见但严重的不良影响，或更严重的影响 在被排除在试验之外的特殊亚组(如孕妇)中常见，或长期使用的影响 以及其他的药物。尽管过去几年探索社交媒体数据的研究有所增加，但 药物警戒，以及它确实可以提出患者观点的证据，没有 为研究目的收集和注释这类数据的系统方法。此次续订建立在我们之前的基础上 研究和自然语言处理(NLP)方法在社交媒体挖掘药物警示 使收集的有关药物使用的社交媒体数据足够精确和系统，从而有助于 研究人员和公众，以及FDA的数据和其他公共信息来源 药物不良事件数据。它提供了自动收集和分析纵向数据的创新方法 健康数据，通过相同的媒体试点干预方法，可以向公众提供信息和帮助 验证自动方法。作为验证，我们包括与现有参考标准的比较 整合FDA的数据和HER数据的不良影响，以及重点关注的具体案例研究(目标3.1) 怀孕期间非类固醇抗炎药和抗抑郁药的使用以及(目标3.2)不遵守的因素。

Project Summary Drugs undergo extensive testing in animals and clinical trials in humans before they are marketed for widespread use. Pre-market testing produces reasonably high quality information about the efficacy of the drug as a treatment for the condition for which it was approved, but gives a very incomplete picture of the drug's safety. It is only after a drug is marketed and used on a more widespread basis over longer periods of time that it is possible to identify other effects, such as rare but serious adverse effects, or those that are more common in the special subgroups excluded from the trial (such as pregnant women), or effects of long-term use of the drug, among others. Despite the increase in research in the past years exploring social media data for pharmacovigilance, and the evidence that it indeed can bring forward the patient perspective, there is no systematic approach to collect and annotate such data for research purposes. This renewal builds on our prior research and natural language processing (NLP) methods for social media mining in pharmacovigilance to make the collection of social media data about medication use precise and systematic enough to be useful to researchers and the public, alongside established sources such as the FDA's data and other public collections of drug adverse event data. It presents innovative methods to automatically collect and analyze longitudinal health data, piloting methods for interventions through the same media that can inform the public and help validate the automatic methods. As validation, we include a comparison to an existing reference standard for adverse effects that integrates FDA's data and HER data, as well as specific case studies focused on (Aim 3.1) the use of NSAIDs and anti-depressants in pregnancy and (Aim 3.2) factors for non-adherence.