使用机器学习预测危机热线聊天中即将发生的自杀风险.
Yossi Levi-Belz1,2, Meytal Grimland3, Yael Segal-Elbak4
1The Lior Tsfaty Center for Suicide and Mental Pain Studies, University of Haifa, Haifa, Israel. Yossil@edu.haifa.ac.il.
Scientific reports
|December 30, 2025
概括
机器学习在实时危机聊天中准确预测即将发生的自杀风险 (IMSR). 关键预测因素包括具体的计划,意图,疼痛耐受性,自我伤害,认知刚性和冲动性,有助于自杀预防工作.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 实时识别自杀风险对于预防至关重要.
- 了解急性自杀危机期间的动态心理过程是具有挑战性的.
- 现有的理论提出了即将发生的自杀风险 (IMSR) 的预测因素,但缺乏实时验证.
研究的目的:
- 调查机器学习在基于互联网的危机热线聊天中预测IMSR的潜力.
- 从自杀危机理论中分析与心理因素相关的语言模式.
- 在危机相互作用期间检查这些因素的预测价值和时间稳定性.
主要方法:
- 对3309个匿名危机聊天会话 (312个被确定为IMSR) 的分析.
- 一个基于自杀危机理论的心理学词典的汇编.
- 提取语言模式,并应用后勤回归模型进行预测分析.
- 时间分析,以评估整个聊天时间的预测器稳定性.
主要成果:
- 具有特定计划和意图的自杀念头是最强的IMSR预测因素.
- 疼痛耐受性,故意自我伤害,认知刚性和冲动性是重要的预测因素.
- 感知到的负担,抑郁症状和情绪疼痛与IMSR有负面关联.
- 大多数已识别的预测因素在聊天期间保持稳定.
结论:
- 通过结合认知,情感和行为因素的综合方法,最好理解IMSR.
- 识别间接风险因素对于实时发现自杀风险至关重要,特别是当意图不明确时.
- 机器学习分析危机聊天的理论理解和实践工具实时干预的进步.
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