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.

项目总结/摘要 阿片类药物使用的戒断症状可能很严重，是复发和 继续滥用。许多阿片类药物使用者正在积极尝试“补救措施”， 他们正在博客和论坛上讨论这些措施的有效性。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，以检查两个国家最近发布的50，000多个帖子 Reddit论坛OpiatesRecovery和Opiates系统地评估正在使用哪些补救措施，如何 它们正在被使用，据报道，这种自助实验的后果是什么。我们将 创建一个用户报告“补救措施”的精选数据库。信息将半自动地从 在线，自我报告的替代治疗的使用（即，其他处方药，非处方药 药物、食物补充剂、冥想和瑜伽等活动）。一组药理学家， 医生和人种学家将评价数据库条目，以发现（1）与以下相关的潜在伤害 不受控制和无人监督的实验，（2）潜在有效的可用治疗（例如，传统 （3）潜在的有前途的化合物电极导线，以及（4）患者的需求和最重要的问题 对他们很重要。 目标1.收集一个广泛的阿片类药物戒断和成瘾相关术语数据库， OpiatesRecovery和Opiates Reddit社区上的帖子。NLP将用于构建语言模型， 理解单词在上下文中的使用方式（word 2 vec）。 目标2.从Reddit开发一个自我报告的补救措施使用实例数据集，并进行一个双方 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，包含与针对戒断的补救措施相关的提取信息， 渴望这一目标将使用人工智能的元素和密切的人类监督来提取补救措施， 包括拼写的变化这一目标的结果将是建立一个补救措施数据库，其中包括： 拼写变化和俚语参考以及将补救措施和用户联系起来的网络分析。 目标3。组织、汇总和系统地评估提及使用补救措施的信息。 潜在的化合物和其他补救措施将被分类，以提供其潜力的初步评估 与阿片类药物治疗过程相关。这一过程将需要最人性化的监督， 考核网络分析工具将用于评估和确定类型之间的关系 的补救措施和潜在的治疗效果，并将建立类似的未来研究的基准。