在QCHAT-10上使用机器学习评估幼儿多元文化自闭症查.
medRxiv : the preprint server for health sciences
|November 28, 2024
概括
使用幼儿自闭症检查清单 (QCHAT-10) 的机器学习模型显示,跨文化早期自闭症谱系障碍 (ASD) 识别具有前途. XGBoost模型取得了高准确度,这表明检查列表是一个有价值的跨文化选工具.
科学领域:
- 计算神经科学是一种计算神经科学.
- 发展心理学是发展心理学.
- 医疗保健中的人工智能
背景情况:
- 早期识别和干预对于改善自闭症谱系障碍 (ASD) 的生活结果至关重要.
- 传统的自闭症诊断方法可能耗时,可能会推迟必要的治疗.
- 幼儿自闭症定量检查清单 (QCHAT-10) 是一种查工具,需要在不同人群中进一步验证.
研究的目的:
- 使用机器学习 (ML) 技术评估QCHAT-10特征的预测价值.
- 评估在不同文化背景下在QCHAT-10数据上训练的ML模型的整体准确性.
- 确定基于ML的ASD查工具在国际数据集中的通用性.
主要方法:
- 在来自波兰,新西兰和沙特阿拉伯的QCHAT-10数据集上训练了ML模型 (决策树,随机森林,XGBoost).
- 使用交叉验证和测试对具有临床诊断标签的独立波兰数据集进行模型性能评估.
- 使用递归特征消除 (RFE) 来识别最具预测性的特征.
主要成果:
- XGBoost的表现始终优于其他模型,实现了高的接收器运行特征曲线 (AUROC) 下面面积值 (例如,在波兰数据上测试的新西兰模型的0.94±0.06).
- 在国际数据上训练的模型在波兰数据集上测试时表现出强大的预测能力,这表明跨文化概括性.
- 特性重要性分析显示了模型中一些共同的预测特征,尽管排名因人口而异.
结论:
- 当使用ML技术分析QCHAT-10时,它显示出作为自闭症谱系障碍跨文化查工具的巨大潜力.
- 基于QCHAT-10响应的ML模型可以有效地预测官方自闭症诊断,即使在不同国家的数据上进行训练.
- 需要进一步的研究来探索特征重要性中的文化差异,并优化不同人群的选工具性能.
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