Online Evidence of Withdrawal Self-Medication

戒断自我药物治疗的在线证据

• 批准号: 9979829
• 负责人: GEORGIY BOBASHEV
• 金额: $ 26.82万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9979829
• 关键词: Acupuncture Therapy; Adult; Artificial Intelligence; Automation; Belief; Benchmarking; Categories; Cluster Analysis; Collaborations; Communities; Data; Data Set; Databases; Drug Prescriptions; Effectiveness; Elements; Epidemiologist; Epidemiology; Food; Food Additives; Food Supplements; Future; Habits; Harm Reduction; Herb; Herbal Medicine; Human; Information Retrieval; Knowledge; Language; Life; Link; Marijuana; Medical; Meditation; Methodology; Methods; Modeling; Natural Language Processing; Non-Prescription Drugs; Opioid; Pathway Analysis; Patient Self-Report; Patients; Pharmaceutical Preparations; Physicians; Pilot Projects; Process; Published Comment; Relapse; Reporting; Research; Resources; Self Medication; Supervision; Terminology; Therapeutic Effect; Traditional Medicine; Twitter; United States Food and Drug Administration; Variant; Vitamins; Withdrawal; Withdrawal Symptom; Yoga; alternative treatment; cost; craving; dietary supplements; epidemiology study; experience; experimental study; interest; non-opioid analgesic; novel; off-label drug; off-label use; online community; opioid misuse; opioid use; opioid user; opioid withdrawal; patient population; self help; social media; spelling; tool; trend

PROJECT SUMMARY/ABSTRACT Withdrawal symptoms from opioid use can be severe and are major contributing factors to relapse and continuing misuse. Many opioid users are actively experimenting with “remedies” that can alleviate withdrawal, and they are discussing their effectiveness in blogs and forums. In this pilot study we will use Natural Language Processing (NLP) and human expertise to examine over 50,000 recent posts in two Reddit forums OpiatesRecovery and Opiates to assess systematically which remedies are being used, how they are being used, and what are the reported consequences of such self-help experimentation. We will create a curated database of user-reported “remedies.” Information will be semiautomatically extracted from the online, self-reported use of alternative treatments (i.e., other prescription drugs, over the counter medications, food supplements, activities such as meditation and yoga). A team of a pharmacologist, physician, and ethnographer will evaluate database entries to uncover (1) potential harm associated with uncontrolled and unsupervised experimentation, (2) potentially effective available treatments (e.g., traditional medicine), (3) potentially promising compound leads, and (4) patients' needs and issues that are most important to them. Aim 1. To assemble an extensive database of opioid withdrawal and remedy-associated terminology from posts on OpiatesRecovery and Opiates Reddit communities. NLP will be used to build a language model that understands how words are used in context (word2vec). Aim 2. To develop a dataset of instances of self-reported remedy use from Reddit and conduct a bipartite network analysis of remedies and users. Using NLP tools and the word embedding model we will develop an exclusive dataset containing extracted information associated with remedies targeting withdrawal and craving. This aim will use elements of artificial intelligence and close human supervision to extract remedies, including variations of spelling, from the texts. The result of this aim will be a remedy database that includes spelling variations and slang references and a network analysis linking remedies and users. Aim 3. To organize, aggregate, and systematically assess information from mentions of remedy use. Potential compounds and other remedies will be classified to provide an initial assessment of their potential relevance to the opioid treatment process. This process will require the most human oversight and assessment. Network analysis tools will be used to assess and identify the relationships between the types of remedies and potential therapeutic effect and will create the benchmarks for similar future studies.

项目摘要/摘要 阿片类药物使用的戒断症状可能很严重，是继电器和接力的主要因素 继续滥用。许多阿片类药物用户正在积极尝试“补救措施”，以减轻 提取，他们正在讨论他们在博客和论坛上的有效性。在这项试点研究中，我们将使用 自然语言处理（NLP）和人类专业知识，可以研究两次的50,000多个最近的帖子 REDDIT论坛的鸦片反应和阿片类药物，以系统地评估哪些补救措施，如何使用 它们正在使用，这种自助实验的报道后果是什么。我们将 创建一个由用户报告的“补救措施”的策划数据库。信息将从半二元中从中提取 在线，自我报告的替代治疗（即其他处方药）在柜台上 药物，食物补充剂，冥想和瑜伽等活动）。一支药理团队， 物理学和民族志学家将评估数据库条目，以发现（1）与之相关的潜在危害 （2）潜在有效的可用治疗方法（例如，传统） 医学），（3）潜在有前途的化合物铅，以及（4）患者的需求和问题最多 对他们来说很重要。 目标1。从 关于鸦片反应和阿片类鸦片的帖子。 NLP将用于构建一种语言模型 了解如何在上下文（Word2Vec）中使用单词。 目的2。开发Reddit的自我报告补救用途实例的数据集并进行两分 补救措施和用户的网络分析。使用NLP工具和单词嵌入模型，我们将开发一个 独家数据集包含与针对撤回和的补救措施相关的提取信息和 渴望。这个目标将使用人工智能的要素并关闭人类的监督来提取补救措施， 包括拼写的变化，来自文本。这个目标的结果将是一个富有的数据库，其中包括 拼写变化和语引用以及链接补救措施和用户的网络分析。 目标3。从记住使用的提及中组织，聚集和系统地评估信息。 潜在的化合物和其他疗法将被归类，以提供对其潜力的初步评估 与阿片类药物治疗过程有关。这个过程将需要最人性的监督，并且 评估。网络分析工具将用于评估和确定类型之间的关系 补救措施和潜在的治疗作用，并将为类似的未来研究创建基准。