Using Named Entity Recognition to Identify Substances Used in the Self-medication of Opioid Withdrawal: Natural Language Processing Study of Reddit Data.
Using Named Entity Recognition to Identify Substances Used in the Self-medication of Opioid Withdrawal: Natural Language Processing Study of Reddit Data.
复制标题
使用命名实体识别来识别阿片类药物戒断自我用药中使用的物质：Reddit数据的自然语言处理研究。
DOI：
10.2196/33919
复制
发表时间：
2022-03-30
影响因子：
2.2
通讯作者：
Bobashev GV
中科院分区：
文献类型：
--
作者：
Preiss A;Baumgartner P;Edlund MJ;Bobashev GV
停止使用阿片类药物可能会导致戒断症状。人们经常继续滥用阿片类药物以避免这些症状。许多使用阿片类药物的人用一系列物质自我治疗戒断症状。对人们使用的物质或其影响知之甚少。这项研究的目的是验证一种方法，用于识别在社交媒体网站Reddit上使用阿片类药物的社区用于治疗阿片类药物戒断症状的物质。我们开发了一个命名实体识别模型，从r/opiates和r/OpiatesRecovery子reddits的近400万条评论中提取物质和效果。为了识别阿片类药物戒断症状的影响以及这些症状的潜在补救措施，我们通过使用聚类和手动审查来消除重复的物质和影响，然后建立了物质和效应共现网络。对于《精神障碍诊断和统计手册》第五版中确定为阿片类药物戒断症状的16种效应中的每一种，我们确定了10种最强烈相关的物质。我们将这些对分类如下：物质是食品和药物管理局批准的或常用的症状治疗，物质不常用于治疗症状，但鉴于其药理学特征，物质可能是潜在有用的，物质是症状的家庭或自然疗法，物质可能导致症状，或其他或不清楚。我们开发了Withdrawal Remedy Explorer应用程序，以促进对数据的进一步探索。我们的命名实体识别模型在保持数据上取得了92.1（物质）和81.7（效果）的F1分数。我们确定了458种独特的物质和235种独特的效果。在130种与戒断症状密切相关的潜在补救措施中，（41.5%）是食品和药物管理局批准的或常用的症状治疗，（13.1%）不常用于治疗症状，但鉴于其药理学特征可能有用，13（10%）是自然或家庭疗法，7例（5.4%）为症状原因，39例（30%）为其他原因或原因不明。我们确定了两种潜在的有希望的补救措施（如加巴喷丁身体疼痛）和潜在的常见但有害的补救措施（如抗组胺药不宁腿综合征）。Reddit用户讨论的许多戒断疗法要么经过临床验证，要么可能有用。这些结果表明，这种方法是一种有效的方式来研究使用阿片类药物的人的基于网络的社区的自我治疗行为。我们的戒断补救措施浏览器应用程序提供了一个平台，用于使用这些数据进行药物警戒，识别新的治疗方法，以及更好地了解阿片类药物戒断患者的需求。此外，这种方法可以应用于许多其他疾病状态，人们自我管理他们的症状，并在网上讨论他们的经验。
The cessation of opioid use can cause withdrawal symptoms. People often continue opioid misuse to avoid these symptoms. Many people who use opioids self-treat withdrawal symptoms with a range of substances. Little is known about the substances that people use or their effects. The aim of this study is to validate a methodology for identifying the substances used to treat symptoms of opioid withdrawal by a community of people who use opioids on the social media site Reddit. We developed a named entity recognition model to extract substances and effects from nearly 4 million comments from the r/opiates and r/OpiatesRecovery subreddits. To identify effects that are symptoms of opioid withdrawal and substances that are potential remedies for these symptoms, we deduplicated substances and effects by using clustering and manual review, then built a network of substance and effect co-occurrence. For each of the 16 effects identified as symptoms of opioid withdrawal in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, we identified the 10 most strongly associated substances. We classified these pairs as follows: substance is a Food and Drug Administration–approved or commonly used treatment for the symptom, substance is not often used to treat the symptom but could be potentially useful given its pharmacological profile, substance is a home or natural remedy for the symptom, substance can cause the symptom, or other or unclear. We developed the Withdrawal Remedy Explorer application to facilitate the further exploration of the data. Our named entity recognition model achieved F1 scores of 92.1 (substances) and 81.7 (effects) on hold-out data. We identified 458 unique substances and 235 unique effects. Of the 130 potential remedies strongly associated with withdrawal symptoms, 54 (41.5%) were Food and Drug Administration–approved or commonly used treatments for the symptom, 17 (13.1%) were not often used to treat the symptom but could be potentially useful given their pharmacological profile, 13 (10%) were natural or home remedies, 7 (5.4%) were causes of the symptom, and 39 (30%) were other or unclear. We identified both potentially promising remedies (eg, gabapentin for body aches) and potentially common but harmful remedies (eg, antihistamines for restless leg syndrome). Many of the withdrawal remedies discussed by Reddit users are either clinically proven or potentially useful. These results suggest that this methodology is a valid way to study the self-treatment behavior of a web-based community of people who use opioids. Our Withdrawal Remedy Explorer application provides a platform for using these data for pharmacovigilance, the identification of new treatments, and the better understanding of the needs of people undergoing opioid withdrawal. Furthermore, this approach could be applied to many other disease states for which people self-manage their symptoms and discuss their experiences on the web.
登录
查看更多内容
影响因子：
3.1
作者：
Everett, M;Borgatti, SP
通讯作者：
Borgatti, SP
影响因子：
4.8
作者：
Bliwise, Donald L.;Zhang, Rebecca H.;Kutner, Nancy G.
通讯作者：
Kutner, Nancy G.
影响因子：
7.4
作者：
Balsamo D;Bajardi P;Salomone A;Schifanella R
通讯作者：
Schifanella R
影响因子：
2.8
作者：
Daniulaityte, Raminta;Falck, Russel;Carlson, Robert G.
通讯作者：
Carlson, Robert G.
影响因子：
4.2
作者：
D'Agostino, Alexandra R.;Optican, Allison R.;Cavazos-Rehg, Patricia A.
通讯作者：
Cavazos-Rehg, Patricia A.