自闭症查项目的紧子集通过QCHAT-10的机器学习分析预测临床诊断
Lydia J Sollis1,2, Dennis P Wall3,4,5, Peter Y Washington6
1Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, 96822, USA.
Scientific reports
|November 7, 2025
概括
使用机器学习的紧型自闭症查工具显示出有希望的结果. 较短的QCHAT-10版本准确地预测了临床自闭症诊断,突出了诸如眼神接触和假装游戏等关键行为标记.
科学领域:
- 神经发育障碍 神经发育障碍
- 医疗保健中的机器学习
- 心理测量 心理测量 心理测量
背景情况:
- 早期识别自闭症谱系障碍 (ASD) 对于改善生活结果至关重要.
- QCHAT-10是一种选工具,但更短的版本可能会提高效率.
- 在不同的临床环境中验证查工具是必不可少的.
研究的目的:
- 确定通过机器学习 (ML) 模型分析的QCHAT-10项目的紧子集是否可以在独立环境中预测临床自闭症诊断.
- 评估在QCHAT衍生标签上训练的ML模型对临床医生确定的诊断的概括性.
- 为了确定强大的自闭症风险行为标志物.
主要方法:
- 机器学习 (ML) 模型在使用新西兰 (n=1054) 和沙特阿拉伯 (n=506) 数据集与QCHAT衍生标签的10个问题QCHAT-10上进行了训练.
- 递归特征消除确定了最佳的四项子集.
- 模型在波兰数据集 (n=252) 上进行了测试,具有独立的临床诊断.
主要成果:
- 四项模型始终确定了眼神接触,注视方向和假装游戏作为关键的预测特征.
- 新西兰训练的模型在测试波兰临床诊断时获得了85%±13的AUROC,而沙特训练的模型在测试波兰临床诊断时获得了87%±11.
- 这些结果表明,从预测评估得分到预测临床诊断的部分可转移性.
结论:
- 通过ML模型分析的紧QCHAT-10子集在预测临床自闭症诊断方面取得了部分成功.
- 眼神接触,眼神追踪和假装游戏成为显著的和可转移的自闭症风险标志物.
- 简化,紧的评估工具有可能实现高效的数字表型和早期识别.
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