Online Evidence of Withdrawal Self-Medication

项目摘要/摘要 使用阿片类药物后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可以缓解 他们正在博客和论坛上讨论他们的有效性。在这项初步研究中，我们将使用 自然语言处理(NLP)和人类专业知识，以在两个月内审查50,000多个最近发布的职位 Reddit论坛操作恢复和阿片类药物，以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，这种自助实验的结果是什么。我们会 为用户报告的“补救措施”创建一个经过管理的数据库。信息将半自动地从 在线、自我报告的替代治疗(即，非处方的其他处方药)的使用 药物、食品补充剂、冥想和瑜伽等活动)。一支药剂学家团队， 医生和人种学家将评估数据库条目，以发现(1)与以下内容相关的潜在危害 无控制和无监督的试验，(2)潜在有效的可用治疗(例如，传统的 医学)，(3)有潜力的复合线索，以及(4)患者的需求和最重要的问题 对他们来说很重要。 目的1.汇编阿片类药物戒断和与药物有关的术语的广泛数据库 Opiats恢复和鸦片Reddit社区上的帖子。NLP将用于构建一种语言模型， 理解单词在上下文中的使用方式(Word2vec)。 目标2.开发来自Reddit的自我报告的补救使用实例的数据集，并进行两部分 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，包含提取的与针对停药的补救措施相关的信息 渴望。这一目标将利用人工智能的元素和密切的人类监督来提取补救措施， 包括文本中的拼写变化。这一目标结果将是一个补救数据库，其中包括 拼写变化和俚语引用，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的药物使用的信息。 将对可能的化合物和其他药物进行分类，以提供对其潜力的初步评估。 与阿片类药物治疗过程的相关性。这一过程将需要最多的人为监督和 评估。将使用网络分析工具来评估和识别类型之间的关系 这是对治疗方法和潜在治疗效果的评估，将为今后的类似研究创造基准。

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.