Social Media Mining for Pharmacovigilance

用于药物警戒的社交媒体挖掘

• 批准号: 10175026
• 负责人: GRACIELA GONZALEZ HERNANDEZ
• 金额: $ 59.42万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10175026
• 关键词: Address; Adverse drug event; Adverse effects; Adverse event; Affect; Agreement; Alcohol consumption; Animals; Antidepressive Agents; Area; Behavior; Case Study; Case-Control Studies; Centers for Disease Control and Prevention (U.S.); Characteristics; Clinical; Clinical Trials; Collaborations; Collection; Complement; Congenital Abnormality; Consent; Control Groups; Data; Data Set; Data Sources; Development; Diagnosis; Drug usage; Electronic Health Record; Evaluation; Event; Fetal Death; Funding; General Population; Goals; Health; Health Care Costs; Health Personnel; Hemorrhage; Hepatic; Human; Hypersensitivity; Intake; Intervention; Kidney; Kidney Failure; Label; Lead; Literature; Live Birth; Long-Term Effects; Low Birth Weight Infant; MEDLINE; Manuals; Marketing; Medical; Methods; Mining; Modeling; Monitor; Natural Language Processing; Non-Steroidal Anti-Inflammatory Agents; Outcome; Patients; Persons; Pharmaceutical Preparations; Physicians; Pregnancy; Pregnancy Outcome; Pregnant Women; Premature Birth; Public Health; Publications; Reference Standards; Reporting; Research; Research Personnel; Safety; Signal Transduction; Smoking; Source; Spontaneous abortion; Standardization; Subgroup; Symptoms; System; Testing; Time; TimeLine; Unified Medical Language System; Validation; Vocabulary; Work; cohort; dosage; drug efficacy; epidemiological model; epidemiology study; health data; indexing; innovation; interest; language processing; medication compliance; medication safety; novel; pharmacovigilance; side effect; social media; systematic review; treatment duration

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.

项目摘要 药物在动物和人类进行临床试验之前进行了广泛的测试 广泛使用。预售测试可产生有关该药物功效的合理高质量信息 作为对其批准条件的治疗，但给出了非常不完整的 药物的安全。只有在较长时期内销售和使用药物销售和使用更广泛的基础之后 可以识别其他效果的时间，例如罕见但严重的不利影响，或者更多的效果 在试验中排除的特殊亚组中常见（例如孕妇）或长期使用的影响 毒品等。尽管过去几年的研究有所增加，探索社交媒体数据 药物保护措施，以及它确实可以提出患者的观点的证据，没有 为了研究目的，收集和注释此类数据的系统方法。这种更新建立在我们先前的 研究和自然语言处理（NLP）方法用于社交媒体采矿中的药物措施 收集有关药物的社交媒体数据，以便精确且系统地进行系统，以便对 研究人员和公众，以及既定的资源，例如FDA的数据和其他公共收藏 药物不良事件数据。它提出了自动收集和分析纵向的创新方法 健康数据，通过相同媒体进行干预的试点方法，这些方法可以告知公众并提供帮助 验证自动方法。作为验证，我们包括与现有的参考标准的比较 整合FDA数据及其数据的不利影响以及针对的特定案例研究（AIM 3.1） NSAID和抗抑郁药在怀孕中的使用和（AIM 3.2）不遵守因素。