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一个简短的观察来选幼儿自闭症,并预测发展轨迹

Fiona Journal^1,2, Thibaut Chataing¹, Michel Godel³

¹Faculty of Medicine, Department of Psychiatry, University of Geneva, Geneva, Switzerland.

Journal of pediatrics. Clinical practice

|January 7, 2026

概括

早期识别自闭症谱系障碍 (ASD) 是可能使用社会沟通行为在三岁以下的儿童. 机器学习准确地预测ASD和认知发展,帮助儿科医生进行早期干预.

科学领域:

发育儿科发育儿科
医疗保健中的机器学习
儿童心理学儿童心理学

背景情况:

自闭症谱系障碍 (ASD) 影响31分之一的儿童,需要早期诊断才能进行有效的干预.
初级保健机构需要有效的工具来识别幼儿的自闭症特征.
早期的社会交流行为是发展查的关键指标.

研究的目的:

评估早期社会沟通行为,使用早期社会沟通尺度 (ESCS) 对三岁以下儿童的自闭症查.
使用基于早期行为的机器学习模型预测儿童的认知发展.
为儿科医生提供一种用于自闭症查和认知发展预测的工具.

主要方法:

对113名患有自闭症儿童和59名典型发育 (TD) 的儿童 (年龄1-3岁) 的纵向分析.
利用23个ESCS变量用于ASD分类和认知发展预测.
使用C5.0决策树用于ASD与TD分类以及认知模式的回归/聚类,使用严格的交叉验证技术.

主要成果:

在区分ASD与TD方面达到95%的准确性,具有96%的灵敏度和92%的特异性.
确定了关键的行为,如轮番启动和指出作为ASD的显著歧视因素.
一个独立的模型准确地 (97%) 按认知结果对儿童进行了分层,行为请求预测了不同的认知轨迹.

结论:

基于早期的社会沟通行为提出了一个原创的决策算法.
这种算法可以指导儿科医生进行自闭症查和认知发展预测.
早期的行为指标为及时和准确的发育评估提供了一个有希望的途径.

关键词:

自闭症自闭症是什么决策树是一个决策树.早期诊断的早期诊断是什么机器学习是机器学习.预测者预测者预测者选工具是一个选工具.

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