在缓刑的青少年中,e-Connect自杀风险分类算法的预测有效性
Casey Sarapas1, Corianna E Sichel2,3, Michael L Dennis1
1Chestnut Health Systems, Bloomington, Illinois.
JAACAP open
|December 10, 2025
概括
一个新的算法有效地预测了青少年的自杀思想和行为 (STBs). 这种风险分类工具有助于识别高风险个体,以便及时干预和治疗联系.
科学领域:
- 精神病学是一个精神病学.
- 犯罪学 犯罪学
- 公共卫生 公共卫生
背景情况:
- 处于少年缓刑的年轻人表现出不成比例的自杀思想和行为 (STBs) 的高率.
- 有效的查和风险分层对于这个脆弱的人群至关重要.
- 现有的系统需要改进的工具来准确评估自杀风险.
研究的目的:
- 评估一个分层自杀风险分类算法的预测有效性,用于预测青少年缓刑者的性病.
- 评估算法在12个月内预测创意,规划,手段收集和尝试的能力.
- 要确定算法是否提供了超出基线STB历史的增量预测值.
主要方法:
- 通过使用个人需求全球评估-初始 (GAIN-I) 的20个项目,对一个档案数据集应用了e-Connect风险分类算法的代理.
- 对4344名未成年试用人员进行了后勤回归分析,并提供了纵向随访数据.
- 这项研究研究了基线风险类别和随后的性病在12个月的时间内之间的关联.
主要成果:
- 基线风险类在12个月内对所有四个测量的STB (构想,规划,手段收集,尝试) 显示出显著的预测能力.
- 观察到一种剂量与反应的关系,更严重的风险类与增加性病风险相关.
- 风险分类算法保留了其预测效用,即使在控制了性病的先前历史之后.
结论:
- 阶级风险分类算法对处于少年缓刑中的青少年的性病具有很强的预测有效性.
- 这些发现支持算法的实用性,作为临床决策支持工具在缓刑设置.
- 这种工具可以为处于危险的年轻人提供有效的选和与适当的自杀预防服务的联系.
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