预测青少年心理健康问题的出现
Jiangyun Hou1,2, Laurens Mortel1,2, Arne Popma1,3
1Department of Psychiatry, University of Amsterdam, Amsterdam, The Netherlands.
Psychological medicine
|April 30, 2025
概括
机器学习模型使用早期亚临床症状准确预测青少年心理健康问题. 这使得有风险的儿童可以制定个性化的预防策略,从而有可能减少未来的残疾.
科学领域:
- 儿童和青少年精神病学
- 医疗保健中的机器学习
- 发展心理学 发展心理学
背景情况:
- 心理健康障碍是青少年残疾的主要原因.
- 目前的方法缺乏工具来识别有风险的年轻人进行早期干预.
- 个性化预防对于缓解青少年心理健康问题至关重要.
研究的目的:
- 开发和验证用于预测青少年精神健康问题的出现的机器学习模型.
- 识别早期指标,包括亚临床症状和神经成像数据,用于风险预测.
- 为高风险儿童提供个性化的预防干预.
主要方法:
- 使用了青少年大脑认知发展 (ABCD) 研究数据.
- 采用机器学习预测6个不同的心理健康问题在1或2年的随访.
- 在发现和验证样本中分析了人口,症状和神经成像数据.
主要成果:
- 9-10岁时的亚临床症状准确预测了12岁时的心理健康问题 (AUC = 0.71-0.90).
- 神经成像数据显示了有限的附加预测价值.
- 模型可以区分外部化和内部化问题发展 (AUC = 0.79).
结论:
- 机器学习模型有效地使用亚临床症状预测儿童时期的心理健康问题转化.
- 这些预测模型支持针对有风险的年轻人进行个性化预防干预.
- 早期识别和干预可能会降低青少年心理健康问题的发生率.
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