使用机器学习分类来识别自闭症和精神分裂症的重叠和独特特征
Jenna N Pablo1, Jorja Shires1, Wendy A Torrens1
1Programs in Cognitive & Brain Sciences, and Neuroscience, University of Nevada, Reno, USA.
Cognitive neuropsychiatry
|February 19, 2025
概括
对自闭症谱系系数 (AQ) 和精神分裂性人格问卷 - 简要修订 (SPQ-BR) 的机器学习分析揭示了自闭症谱系障碍 (ASD) 和精神分裂性谱系障碍 (SSD) 之间的重叠症状. 模型失败发现了独特的因素,有助于诊断和生物标志物发现.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 计算精神病学是一种计算精神病学.
背景情况:
- 自闭症谱系障碍 (ASD) 和精神分裂症谱系障碍 (SSD) 分享重叠的症状.
- 区分这些疾病对于准确的诊断和治疗至关重要.
- 现有的研究选,如自闭症谱系系数 (AQ) 和精神分裂性人格调查问卷 - 简要修订 (SPQ-BR),在非临床和亚临床人群中广泛使用.
研究的目的:
- 通过机器学习,调查AQ和SPQ-BR是否可以识别ASD和SSD之间的非重叠症状.
- 确定症状重叠的程度,并确定两种疾病之间的特征.
- 为使用这些选器提供指导,以提高诊断准确度和生物标志物识别.
主要方法:
- 一组1397名本科生完成了SPQ-BR和AQ.
- 采用随机森林分类模型来评估项目得分与AQ和SPQ-BR的因素之间的预测关系.
- 模型使用所有项目分数进行训练,随后分析了最小和最重要的特征.
主要成果:
- 强大的特征重叠得到证实,使可从SPQ-BR得分预测AQ得分,反之亦然.
- AQ项目得分预测了SPQ-BR的无组织和人际关系因素.
- SPQ-BR项目得分预测了QA的沟通和社交技能因素.
- 最重要的是,分类模型的失败表明,AQ项目得分无法预测SPQ-BR认知感知因子,SPQ-BR项目得分无法预测AQ的想象力,细节注意力或注意力切换因子.
结论:
- SPQ-BR和AQ有效地测量重叠的症状,可以区分一些因素.
- 模型失败突出了特定的,不重叠的症状域,这些症状域是每个病情独有的.
- 这些发现为利用现有的查工具提供了一个框架,以防止误诊,并推进对ASD和SSD的特定生物标志物的识别.
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