在日本用户生成的数据中监测非处方药物滥用
Tomohiro Nishiyama1, Shuntaro Yada2, Shoko Wakamiya1
1Division of Information Science, Nara Institute of Science and Technology, Japan.
Studies in health technology and informatics
|August 8, 2025
概括
这项研究开发了一个系统来检测社交媒体上的非处方药 (OTC) 滥用,通过可视化药物使用趋势来帮助公共卫生.
科学领域:
- 公共卫生 公共卫生
- 计算语言学 计算语言学
- 药物监督 药物监督 药物监督
背景情况:
- 非处方药 (OTC) 的滥用是全球重要的公共卫生问题.
- 社交媒体数据为监测药物使用模式提供了一个潜在的来源.
研究的目的:
- 在社交媒体数据中开发和评估一个用于检测和可视化不适当的OTC药物使用的系统.
- 解决日本社交媒体中的语言和文化细微差别,以准确检测滥用.
主要方法:
- 构建了一个标记的 20,036 个日本推特,包括 7,000 个与药物相关的帖子.
- 从变压器 (BERT) 模型中微调的日本双向编码器表示,用于识别滥用模式 (例如过度使用).
- 集成了一个可视化工具,以显示OTC药物使用的时间和分类趋势.
主要成果:
- 该系统在识别特定的OTC药物滥用模式方面表现强.
- 可视化工具提供了对药物滥用的时间和分类趋势的见解.
- 错误分析强调了含糊不清的术语和社交媒体数据中的噪音带来的挑战.
结论:
- 该模型在有足够数据的情况下显示出有效性,但在代表性不足的类别中面临局限性.
- 需要改进语境理解和先进的技术,如更大的语言模型或数据增强.
- 该系统用于检测OTC药物滥用和可视化趋势的方法对实时药物监测有希望,并且可以适应其他语言.
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