用他们自己的话说:青少年智能手机语言在与自杀有关的住院治疗之前的案例研究
Isaac N Treves1, Paul A Bloom2, Samantha Salem2
1Department of Psychiatry, Columbia University, New York, US. isaac.treves@nyspi.columbia.edu.
NPP - digital psychiatry and neuroscience
|March 2, 2026
概括
智能手机的被动传感检测到在急性自杀危机期间,青少年的自杀相关语言和负面情绪增加. 需要进一步的上下文分析来准确检测风险.
科学领域:
- 数字化表型化是指数字化表型化.
- 计算精神病学是一种计算精神病学.
- 青少年心理健康 青少年心理健康
背景情况:
- 青少年自杀率正在上升,需要改进检测短期自杀风险的方法.
- 智能手机对用户语言的被动感知为实时风险评估提供了一个潜在的途径.
- 在急性自杀危机期间智能手机传感的有效性需要进一步研究.
研究的目的:
- 检查自然语言处理 (NLP) 分析智能手机文本数据的实用性,以检测危机期间青少年的自杀风险.
- 为了评估语言,情绪和文本条目中主题的变化,先前的精神病医院住院自杀危机.
主要方法:
- 一个涉及五名因自杀危机住院的青少年的案例研究.
- 分析了每位参与者使用NLP对与自杀相关的内容,情绪和主题进行的超过21000条输出文本条目.
- 临床医生对文本条目进行定性审查,以识别潜在的风险事件并将NLP发现置于背景.
主要成果:
- 五分之四的青少年在住院前10天表现出更多与自杀有关的语言和负面情绪.
- 提升的自杀语言在住院5天内达到顶峰,而负面情绪在5-10天前达到顶峰.
- 在急性风险时期之外,NLP信号也出现了,这表明区分自杀风险与一般困境的挑战.
结论:
- 智能手机语言分析显示,在识别急性自杀风险的青少年方面有希望,在住院之前出现特定的语言标记.
- NLP信号与自杀的想法和行为相关,并可以识别相关的话题,如物质使用和治疗.
- 局限性包括NLP在推断上下文方面的困难,突出了为个性化风险检测需要增强的上下文分析的需要.
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