Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
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DOI：
10.1007/s10803-015-2379-8
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发表时间：
2015-07-01
影响因子：
3.9
通讯作者：
Castiglioni, Isabella
中科院分区：
文献类型：
--
作者：
Crippa, Alessandro;Salvatore, Christian;Castiglioni, Isabella
在目前的工作中，我们进行了一项概念验证研究，以确定简单的上肢运动是否有助于准确分类 2-4 岁患有自闭症谱系障碍 (ASD) 的低功能儿童。为了回答这个问题，我们开发了一种监督机器学习方法，通过对简单的伸手到放下任务的运动学分析，正确区分 15 名患有 ASD 的学龄前儿童和 15 名正常发育的儿童。我们的方法通过与运动的目标导向部分相关的七个特征达到了 96.7% 的最大分类准确率。这些初步研究结果提供了对自闭症谱系障碍（ASD）可能的运动特征的深入了解，这可能有助于识别明确的患者子集，从而减少广泛行为表型内的临床异质性。
In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2-4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7 % with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype.