识别自杀念头的计算方法:一个系统的审查
Brahian Stiven Gil Arias1, Juan Carlos Blandón Andrade1, Grigori Sidorov2
1Programa de Ingeniería de Sistemas y Telecomunicaciones, Universidad Católica de Pereira, Pereira, Colombia.
Frontiers in artificial intelligence
|February 9, 2026
概括
本研究回顾了用于检测文本中自杀念头的计算技术. 像BERT这样的基于变压器的模型显示出早期预防的希望,但需要更多多样化的数据.
科学领域:
- 计算语言学计算语言学
- 公共卫生 公共卫生
- 人工智能的人工智能是人工智能.
背景情况:
- 自杀是一个主要的公共卫生问题,特别是在年轻人中,在COVID-19大流行期间发生率增加.
- 自然语言处理 (NLP) 提供了分析文本预测自杀想法的工具.
- 早期发现和干预对于自杀预防策略至关重要.
研究的目的:
- 系统地审查用于识别自然语言文本中自杀念头的计算技术.
- 提取和综合用于自杀风险检测的NLP方法的发现.
- 识别自杀念头检测计算方法的当前趋势和局限性.
主要方法:
- 基于PRISMA 2020方法的系统文献审查.
- 在主要的学术数据库 (Scopus,IEEE Xplore,ACM,Springer,Web of Science) 进行的搜索.
- 使用叙事合成进行定性分析;使用AMSTAR 2评估偏差风险.
主要成果:
- 确定了25项关于用于检测自杀念头的计算方法的研究.
- 基于变压器的模型 (例如BERT) 和混合方法 (BERT与CNN/LSTM) 是主要的.
- 像TF-IDF和预训练嵌入等技术用于提高性能.
结论:
- 计算技术显示了早期发现和预防自杀念头的巨大潜力.
- 局限性包括缺乏语言/文化多样性和过度依赖社交媒体数据.
- 进一步的研究应侧重于各种数据集,并改善非专家的模型解释性.
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