Mining Social Network Postings for Mentions of Potential Adverse Drug Reactions

挖掘社交网络帖子中提及潜在药物不良反应的内容

• 批准号: 8222740
• 负责人: GRACIELA GONZALEZ HERNANDEZ
• 金额: $ 36.19万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-10 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8222740
• 关键词: Active Learning; Address; Adverse drug effect; Adverse effects; Adverse event; Adverse reactions; Age; Algorithms; Attention; Award; Case Study; Cause of Death; Characteristics; Classification; Clinical Trials; Complement; Computer software; Data; Data Sources; Databases; Development; Diagnosis; Disease; Drug usage; Electronic Health Record; Epidemiology; Evaluation; Exclusion Criteria; Goals; Gold; Health; Health Professional; Human; Insurance; Intervention; Knowledge; Long-Term Effects; Machine Learning; Maps; Marketing; Measures; Methods; Mining; Monitor; Natural Language Processing; Patient Self-Report; Patients; Performance; Pharmaceutical Preparations; Phase; Physicians; Population; Predictive Value; Preparation; Process; Public Health; Published Comment; Reaction; Reporting; Research Design; Research Infrastructure; Research Personnel; Safety; Scheme; Semantics; Sensitivity and Specificity; Sentinel; Signal Transduction; Social Network; Source; Subgroup; System; Techniques; Terminology; Testing; Text; Time; Training; Unified Medical Language System; Variant; Work; base; case control; clinical trials in animals; cohort; drug development; drug efficacy; follow-up; innovation; language processing; lexical; non-compliance; post-market; programs; prototype; social networking website; tool

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.

描述（由申请人提供）： 药物在动物和人类的临床试验中进行广泛的测试，然后才能在人群中广泛使用。预装前测试可产生有关该药物作为批准状况的治疗疗效的合理高质量信息，但对药物的安全性有很不完整的情况。市场后的监视目前主要依赖于医疗保健专业人员（以及最近的患者本身）通过FDA的安全信息和不良事件报告计划MedWatch向FDA的自愿报告。自我报告的患者信息捕获了一种有价值的观点，该观点与卫生专业人员提供的质量相似，目前仅通过正式的MedWatch表格捕获。该应用程序的总体目标是部署探索非正式社交网络帖子的价值所需的基础设施，作为药物上市后潜在不良药物反应的“信号”的来源必须比目前可能更早检测不良事件，并且要检测不容易通过传统手段捕获的效果。尽管处理口语文本的巨大挑战，但我们朝这个方向的原型研究表明，在识别这些文章中提到的不良反应方面表现出色，公众提到的影响与我们研究的药物的效果之间存在显着相关性。要解决的具体目标包括：1）。建立能够在与健康相关的社交网络网站上处理有关该药物的在线用户评论的基础架构。特别是，我们寻求在这些非正式帖子中认识并提及不利影响，并将其映射到标准术语中。我们将基于我们的初步词汇方法来查找提及，并提出了机器学习的变化（通常称为主动学习），在该方法中，机器学习框架可以控制在培训数据中选择哪些实例，除了其他创新的语义方法来进行标准化（将提及映射到建立的正式术语）和情感分析（以发现提及是报告正面还是负面影响）； 2）评估提取和识别系统的敏感性和特异性，以及通过对一组具有众所周知不良反应的药物的特定案例研究提取知识的预测价值，并通过监视有关释放的一组药物的发布自2007年以来。我们现有的手动注释的黄金标准将通过由药理学家（Karen Smith）领导的专门注释工作来扩展。 3）将从患者评论中提取的知识与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，通过已部署的基础架构获得的数据将无法自行定义ADR。但是，如果该方法已验证，则可以提供有用的信号来补充已经建立的流程和数据源。