Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning
Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning
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DOI：
10.1016/j.addbeh.2016.08.019
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发表时间：
2017-02-01
影响因子：
4.4
通讯作者：
Mackey, Tim K.
中科院分区：
文献类型：
--
作者：
Kalyanam, Janani;Katsuki, Takeo;Mackey, Tim K.
简介：处方药/药物的非医疗使用 (NMUPD) 是一个严重的公共卫生威胁，特别是与处方阿片类镇痛药滥用流行相关。尽管人们对这个问题的关注不断增加，但仍然迫切需要在“数字流行病学”领域制定新策略，以更好地识别、分析和理解 NMUPD 行为趋势。 方法：我们通过收集针对三种常见滥用的处方阿片类镇痛药 Percocet（对乙酰氨基酚/羟考酮）、OxyContin（羟考酮）和羟考酮过滤的 1100 万条推文，对流行的微博网站 Twitter 进行监测。对每种镇痛药物的推文子集应用无监督机器学习，以发现有关风险行为的潜在潜在主题。在随后的三轮机器学习中，执行了获取主题和过滤掉不需要的推文的两步过程。结果：使用这种方法，识别出了 230 万条推文，其中包含与镇痛 NMUPD 相关的内容。确定了每种药物的基本主题，并针对 NMUPD 行为风险因素注释了每个主题最具代表性的推文。主要主题确定了推特上有关多种药物滥用的社交媒体高水平讨论的证据。其中特别提到了各种多种药物组合，包括使用其他类别的处方药和非法药物滥用。结论：这项研究提出了一种过滤 Twitter 内容以了解 NMUPD 行为的方法，同时还以最少的人为干预识别潜在主题。研究结果准确跟踪用于隔离感兴趣的 NMUPD 相关风险行为的纳入/排除标准，并且还提供了对具有高社交媒体参与度的 NMUPD 行为的见解。结果表明，与依赖内容分析和人类编码方案的其他研究相比，这可能是一种用于大数据药物滥用监测、数据收集和分析的可行方法。 (C) 2016 Elsevier Ltd. 保留所有权利。
Introduction: Nonmedical use of prescription medications/drugs (NMUPD) is a serious public health threat, particularly in relation to the prescription opioid analgesics abuse epidemic. While attention to this problem has been growing, there remains an urgent need to develop novel strategies in the field of "digital epidemiology" to better identify, analyze and understand trends in NMUPD behavior.Methods: We conducted surveillance of the popular microblogging site Twitter by collecting 11 million tweets filtered for three commonly abused prescription opioid analgesic drugs Percocet (acetaminophen/oxycodone), OxyContin (oxycodone), and Oxycodone. Unsupervised machine learning was applied on the subset of tweets for each analgesic drug to discover underlying latent themes regarding risk behavior. A two-step process of obtaining themes, and filtering out unwanted tweets was carried out in three subsequent rounds of machine learning.Results: Using this methodology, 2.3M tweets were identified that contained content relevant to analgesic NMUPD. The underlying themes were identified for each drug and the most representative tweets of each theme were annotated for NMUPD behavioral risk factors. The primary themes identified evidence high levels of social media discussion about polydrug abuse on Twitter. This included specific mention of various polydrug combinations including use of other classes of prescription drugs, and illicit drug abuse.Conclusions: This study presents a methodology to filter Twitter content for NMUPD behavior, while also identifying underlying themes with minimal human intervention. Results from the study track accurately with the inclusion/exclusion criteria used to isolate NMUPD-related risk behaviors of interest and also provides insight on NMUPD behavior that has a high level of social media engagement. Results suggest that this could be a viable methodology for use in big data substance abuse surveillance, data collection, and analysis in comparison to other studies that rely upon content analysis and human coding schemes. (C) 2016 Elsevier Ltd. All rights reserved.