用人工智能区分青少年自杀和非自杀自我伤害:超越自杀意图和自杀能力
1Department of Psychology, University of Oslo, Norway.
Journal of affective disorders
|February 15, 2025
概括
区分青少年自杀性自我伤害 (SSH) 和非自杀性自我伤害 (NSSH) 是可能的,使用整体的多模型域分析 (HMDA). 这种方法有效地区分SSH和NSSH,而不需要评估自杀意图或能力.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 青少年心理学 青少年心理学
- 公共卫生 公共卫生
背景情况:
- 青少年自杀性自我伤害 (SSH) 和非自杀性自我伤害 (NSSH) 之间的临床区分是一个重大挑战.
- 仅基于临床表现来区分这些行为的可行性仍在争论中.
研究的目的:
- 确定青少年SSH是否可以使用整体多模型域分析 (HMDA) 从NSSH区分出来.
- 评估HMDA与主要自杀理论的兼容性,而不依赖于意图或获得的能力.
- 探索与SSH和NSSH相关的风险和保护因素.
主要方法:
- 一项采用2017-2019年挪威全国调查数据的横截面研究.
- 包括26,904名青少年 (平均年龄15.01,男性29.9%) 自报自伤.
- 应用元学习者分类模型和探索性因子分析.
主要成果:
- 该分类模型在区分SSH和NSSH时实现了77.0%的AUC和70.0%的灵敏度/特异性.
- 探索性因素分析确定了八个关键因素:饮食失调/未来导向,睡眠,影响/自我导向,冒险,同行关系,安全/安全,家庭关系和物质使用.
- 这些因素凸显了幸福领域在预测自我伤害行为的重要性.
结论:
- 青少年SSH和NSSH是不同的,多方面的疾病,可以在不依赖自杀意图或能力信息的情况下进行区分.
- 这些发现支持青少年自杀倾向和自我毁灭理论,并部分支持思想转化为行动理论.
- HMDA提供了关于青少年自我伤害风险和保护指标的宝贵见解.
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