Mining Social Network Postings for Mentions of Potential Adverse Drug Reactions

描述(由申请人提供)： 药物在进入市场并在人群中广泛使用之前，要经过广泛的动物试验和临床试验。上市前测试提供了相当高质量的信息，说明该药物作为一种治疗药物的有效性，但对该药物的安全性提供了非常不完整的图景。目前，上市后监测主要依赖于卫生保健专业人员(最近还包括患者自己)通过FDA的安全信息和不良事件报告计划MedWatch向FDA自愿报告。自我报告的患者信息捕获了一个有价值的视角，已被发现具有与卫生专业人员提供的类似质量的观点，目前仅通过正式的MedWatch表单获取。该应用程序的总体目标是部署所需的基础设施，以探索非正式社交网络帖子的价值，将其作为药物上市后不久潜在不良反应的“信号”来源，特别关注此类信息可能必须比当前可能的更早发现不良事件的价值，以及检测传统方法不易捕获的效果。尽管处理口语文本存在重大挑战，但我们在这方面的原型研究在识别这些帖子中提到的不良反应方面表现出了良好的性能，公众提到的效果与我们研究的药物的记录效果之间存在显著的相关性。要解决的具体目标包括：1)。建立基础设施，以便能够处理与健康相关的社交网络网站上的在线用户对该药物的评论。特别是，我们试图识别和提取在这些非正式帖子中提到的不利影响，并将其映射到标准术语。我们将在找到提及的初步词汇方法的基础上，提出一种机器学习的变体(通常称为主动学习)，其中机器学习框架具有控制将选择哪些实例用于训练数据的能力，以及其他标准化(将提及映射到已建立的正式术语)和情感分析(以发现提及是报告积极的还是消极的影响)的创新语义方法；2)通过对一组具有众所周知不良反应的药物的具体案例研究，以及通过监测关于2007年以来发布的一组选定药物的公告，评估提取和鉴定系统的敏感性和特异性，以及提取的知识的预测价值。我们现有的手动注释黄金标准将通过由药理学家(Karen Smith)领导的专门注释工作进行扩展。3)将从患者意见中提取的知识与FDA监督的既定药物安全监测计划得出的知识进行比较。我们认识到，通过部署的基础设施获得的数据不能用来定义独立的ADR。然而，如果这种方法得到验证，它可以提供有用的信号来补充已经建立的进程和数据来源。

DESCRIPTION (provided by applicant): Drugs undergo extensive testing in animals and clinical trials in humans before they are marketed for widespread use in the population. 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. Post-marketing surveillance currently relies mainly on voluntary reporting to the FDA by health care professionals (and recently, patients themselves) through MedWatch, the FDA's safety information and adverse event reporting program. Self-reported patient information captures a valuable perspective that has been found to be of similar quality to that provided by health professionals, and currently it is only captured via the formal MedWatch form. The overarching goal of this application is to deploy the infrastructure needed to explore the value of informal social network postings as a source of "signals" of potential adverse drug reactions soon after the drugs hit the market, paying particular attention at the value such information might have to detect adverse events earlier than currently possible, and to detect effects not easily captured by traditional means. Despite the significant challenge of processing colloquial text, our prototype study in this direction showed promising performance in identifying adverse reactions mentioned in these postings, with significant correlations between the effects mentioned by the public and those documented for the drugs we studied. Specific aims to be addressed include: 1). To establish the infrastructure that enables processing of online user comments about the drug on health-related social network websites. Particularly, we seek to recognize and extract mentions of adverse effects in those informal postings, and to map them to standard terminology. We will build on our preliminary lexical approach for finding the mentions, and propose a variation of machine learning (commonly referred to as active learning) where the machine learning framework has the ability to control what instances will be selected for use in the training data, among other innovative semantic approaches to normalization (mapping of the mentions to established, formal terms) and sentiment analysis (to discover whether a mention is reporting a positive or a negative effect); 2) To evaluate the sensitivity and specificity of the extraction and identification systems, as well as the predictive value of the extracted knowledge through specific case studies of a set of drugs with well known adverse reactions and by monitoring postings about a select group of drugs released since 2007. Our existing manually annotated gold standard will be expanded through a dedicated annotation effort led by a pharmacologist (Karen Smith). 3) To compare the knowledge extracted from patient comments to what is derived from the established drug safety monitoring scheme overseen by the FDA. We recognize that the data obtained through the deployed infrastructure would not be able to be used to define an ADR standing on its own. However, if this method is validated, it could provide useful signals to complement the already established processes and data sources.