Social Media Mining for Pharmacovigilance

用于药物警戒的社交媒体挖掘

• 批准号: 10407315
• 负责人: GRACIELA GONZALEZ HERNANDEZ
• 金额: $ 13.71万
• 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/10407315
• 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 药物在上市前要经过广泛的动物试验和人体临床试验。 广泛使用。上市前测试产生了关于药物疗效的合理高质量信息 作为一种治疗的条件，它被批准，但给出了一个非常不完整的图片， 药物的安全性。只有当一种药物在更长的时间内被更广泛地销售和使用后， 有可能确定其他影响的时间，例如罕见但严重的不良反应，或那些更严重的不良反应。 常见于从试验中排除的特殊亚组（如孕妇），或长期使用的影响 药物，除此之外。尽管在过去几年中，探索社交媒体数据的研究有所增加， 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此类数据的系统方法。这次更新建立在我们之前 研究和自然语言处理（NLP）方法，用于药物警戒中的社交媒体挖掘， 使有关药物使用的社交媒体数据的收集足够精确和系统， 研究人员和公众，以及已建立的来源，如FDA的数据和其他公共收集， 药物不良事件数据。它提出了创新的方法来自动收集和分析纵向 健康数据，通过可以告知公众并帮助 验证自动方法。作为验证，我们包括与现有参比标准品的比较， 整合FDA数据和HER数据的不良反应，以及重点关注的特定病例研究（目标3.1） 妊娠期使用NSAID和抗抑郁药以及（目标3.2）不依从因素。