使用多模式风险因素预测青少年首次出现自杀想法和行为:为期四年的纵向研究
Josh Nguyen1, Dom Dwyer1, Yara J Toenders2
1The University of Melbourne, Melbourne, Australia; Orygen, Parkville, Australia.
概括
这项研究开发了机器学习模型来预测青少年的自杀思想或行为 (STB),达到中等准确度. 关键预测因素包括女性性别,睡眠问题和不利环境,突出了早期干预的领域.
科学领域:
- 青少年精神病学 青少年精神病学
- 机器学习在医疗保健中的应用
- 发展心理学是发展心理学.
背景情况:
- 自杀想法或行为 (STB) 是全球年轻人死亡的主要原因.
- 预测性病发病的先前研究在数据模式和人口集中 (成年人) 上存在局限性.
研究的目的:
- 为了在四年内前性地预测青少年的第一次性病发作.
- 利用既有STB分类模型,又有新型机器学习模型,包括195个生物心理社会特征.
主要方法:
- 利用了来自青少年大脑认知发展 (ABCD) 项目的7503名青少年 (9-11岁) 的数据.
- 应用了现有的STB历史分类模型,并训练了一种新的弹性净物流回归模型,具有195个特征.
- 在独立网站上验证了新型号的前15个功能.
主要成果:
- 两种模型都显示了第一次出现的STB的中度预测准确性 (AUC从0.63到0.73).
- 一致的预测因素包括女性性别,睡眠障碍和不适应的家庭和学校环境.
- 还确定了新的神经认知和脑成像风险因素.
结论:
- 开发的模型在预测青少年首次出现的性病时提供了中度的准确性.
- 这项研究加强了已知的心理风险因素,并引入了新的潜在神经生物学标志物.
- 在临床应用之前,建议对多样化,大规模样本进行进一步验证.
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