检查最重要的风险因素来预测青少年持续和痛苦的精神病般的经历
Nicole R Karcher1, Aristeidis Sotiras2, Tara A Niendam3
1Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri.
概括
持续的令人痛苦的精神病类经历 (PLEs) 最好通过内化症状和不良的童年经历来预测. 机器学习准确地识别了这些因素,帮助临床相关的PLE进行早期干预.
科学领域:
- 发展心理学是发展心理学.
- 神经科学是一个神经科学.
- 心理病理学 心理病理学
背景情况:
- 临床上显著的精神病类经历 (PLEs) 的特点是持久性和痛苦.
- 对临床相关的PLEs的风险因素的早期识别对于了解它们的病因产生至关重要.
- 机器学习可以分析基线因素,以区分持续的困扰PLE.
研究的目的:
- 确定主要基线因素,使持久性痛苦的PLE与低级别和短暂的痛苦的PLE区分开来.
- 应用机器学习模型来预测青少年临床相关的PLEs.
- 了解认知,神经,发育和经验因素在PLE中的作用.
主要方法:
- 利用了青少年大脑认知发展 (ABCD) 研究的数据.
- 开发了随机森林分类模型,以区分PLE组 (持久的困扰,暂时的困扰,低级).
- 输入特征包括认知功能,神经测量 (皮层厚度,功能连接),发育迟缓,内化症状和不良童年经历.
主要成果:
- 区分持久性痛苦的PLE与低级别的PLE的模型实现了69.33%的准确性.
- 内化症状,不良童年经历和认知功能是持续痛苦的PLEs最重要的预测因素.
- 区分持久和短暂的痛苦PLE的模型表现有限.
结论:
- 不利的童年经历是持续痛苦的PLEs的特别重要的预测因素.
- 机器学习有效地区分了临床上最和最不相关的PLE组.
- 这些模型基于PLE持久性的临床相关群体之间的区分能力有限.
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