自杀分类模型中的错误阳性是风险组吗? 在对挪威青少年进行的一项人口代表性的纵向研究中,证据证明了"真正的警报"
E F Haghish1, Bruno Laeng1,2, Nikolai Czajkowski1,3
1Faculty of Social Sciences, Department of Psychology, University of Oslo, Oslo, Norway.
Frontiers in psychology
|October 2, 2023
概括
自杀尝试分类模型中的错误阳性实际上对未来的尝试有更高的风险. 这些"真正的警报"表明需要纳入青少年自杀预防计划.
科学领域:
- 精神病学是一个精神病学.
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 用于自杀企图分类的机器学习模型通常会产生错误的阳性结果.
- 这些错误的阳性结果通常被认为是分类错误.
- 然而,他们可能代表未来风险较高的个体.
研究的目的:
- 调查自杀企图分类中的错误阳性是否对未来自杀企图的风险更高.
- 检查增加特异性值如何影响与虚假阳性相关的风险.
- 为了比较假阳性和真阳性之间的风险因素的严重程度,在不同的特异性水平.
主要方法:
- 利用了11,369名挪威青少年的大型基于人口的纵向数据集.
- 采用渐变增强算法来对自杀企图进行分类.
- 在第二个时间点对虚假阳性和真负性的自杀企图风险进行评估,具体性值各不相同.
主要成果:
- 与真负相相比,假阳性表明未来自杀企图的风险明显更高.
- 增加特异性值 (60%至97.5%) 放大了虚假阳性的相对自杀企图风险 (2.96至7.22倍).
- 较高的风险值导致假阳性和真阳性之间风险因素的严重程度更为可比.
结论:
- 机器学习自杀分类模型的临床相关性,特别是假阳性,应该优先考虑,而不是仅仅是分类错误指标.
- 所谓的假阳性代表了一个高风险群体,需要纳入自杀预防计划.
- 这些发现需要重新评估机器学习模型的解释和青少年自杀预防策略的规划.
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