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相关概念视频

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Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
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Drug dependence, abuse, and addiction are complex phenomena that can precipitate various abnormal states. Physical dependence refers to a state of pharmacological adaptation to a drug. This adaptation often results in tolerance—a reduced response to the drug after repeated administrations. When the drug use is abruptly stopped, withdrawal symptoms occur due to the body's need to readjust from the pharmacologically induced imbalance. However, tolerance and withdrawal symptoms do not...
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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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使用社交媒体数据和暗网检测物质使用障碍:时间和知识意识研究

Usha Lokala1, Orchid Chetia Phukan2, Triyasha Ghosh Dastidar3

  • 1Department of Computer Science and Computer Engineering, Artificial Intelligence Institute, University of South Carolina, Columbia, SC, United States.

JMIRx med
|May 8, 2024
PubMed
概括

这项研究分析了有关合成阿片类药物的社交媒体帖子,揭示了用户的各种情绪和情绪. 调查结果为打击阿片类药物危机的公共卫生干预提供了洞察力.

关键词:
美国 美国 美国美国的美国.对于加密货币,加密货币就是加密货币.黑暗的网络 黑暗的网络芬太尼 (Fentanyl) 是一种药物.心理健康 心理健康这是一种阿片类阿片类药物.在阿片类药物危机中,阿片类药物危机正在发生.社交媒体 社交媒体滥用物质是因为滥用物质.使用物质使用物质.药物使用障碍物使用障碍物.有关合成阿片类药物用户感知用户的感知.使用者使用者使用者使用者

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科学领域:

  • 公共卫生 公共卫生
  • 计算社会科学 计算社会科学
  • 数据科学数据科学数据科学

背景情况:

  • 阿片类药物危机是美国重要的公共卫生问题.
  • 了解用户对合成阿片类药物的看法对于开发有效干预措施至关重要.
  • 关于物质滥用与心理健康之间的直接关系的证据有限,这影响了治疗的可获得性.

研究的目的:

  • 分析有关在加密市场上销售的物质使用和阿片类药物的社交媒体帖子.
  • 应用深度学习模型来衡量用户对各种合成阿片类药物的情绪和情绪.
  • 确定特定药物与使用者的情绪反应之间的相关性,包括恐惧,悲伤和乐观.

主要方法:

  • 利用药物滥用本体学和先进的深度学习模型,包括双向编码器从变压器 (BERT) 的表示.
  • 抓取了加密货币市场的数据,提取了与芬太尼,其类似物及其新型合成阿片类药物相关的帖子.
  • 对情绪和情绪进行主题分析,将其与与药物相关的主题相关联,并使用时意识的神经模型.

主要成果:

  • 最有效的深度学习模型在识别物质使用障碍时获得了82.12的宏观F1得分和83.58的回忆.
  • 识别了与不同合成阿片类药物相关的独特情绪和情绪反应.
  • 与疼痛缓解,成和戒断症状等主题相关的用户响应.

结论:

  • 通过社交媒体情绪分析,提供了关于公众对合成阿片类药物的看法的宝贵见解.
  • 调查结果可以为公共卫生政策和干预措施提供信息,以缓解物质滥用和阿片类药物危机.
  • 证明了深度学习在公共卫生研究中分析社交媒体数据的有效性.