Objectives: To investigate the efficacy and required indicators of Children Neuropsychological and Behavioral Scale-Revision 2016 (CNBS-R2016) in the differential diagnosis of autism spectrum disorder (ASD) and global developmental delay (GDD).
Methods: A total of 277 children with ASD and 415 children with GDD, aged 18-48 months, were enrolled as subjects. CNBS-R2016 was used to assess the developmental levels of six domains, i.e., gross motor, fine motor, adaptive ability, language, social behavior, and warning behavior, and a total of 13 indicators on intelligence age and developmental quotient (DQ) were obtained as the input features. Five commonly used machine learning classifiers were used for training to calculate the classification accuracy, sensitivity, and specificity of each classifier.
Results: DQ of warning behavior was selected as the first feature in all five classifiers, and the use of this indicator alone had a classification accuracy of 78.90%. When the DQ of warning behavior was used in combination with the intelligence age of warning behavior, gross motor, and language, it had the highest classification accuracy of 86.71%.
Conclusions: Machine learning combined with CNBS-R2016 can effectively distinguish children with ASD from those with GDD. The DQ of warning behavior plays an important role in machine learning, and its combination with other features can improve classification accuracy, providing a basis for the efficient and accurate differential diagnosis of ASD and GDD in clinical practice.
目的: 探索儿童神经心理行为检查量表2016版（以下简称“儿心量表”）鉴别孤独症谱系障碍（autism spectrum disorder, ASD）和全面发育迟缓（global developmental delay, GDD）的效能及其所需指标。方法: 回顾性选取18~48月龄的ASD（n=277）和GDD（n=415）患儿为研究对象，采用儿心量表评估两组儿童在大运动、精细运动、适应能力、语言、社会行为、警示行为6大能区的发育水平，并将获得的智龄和发育商（developmental quotient, DQ）共13个指标的数据作为特征，应用5种机器学习（machine learning, ML）分类器进行模型训练，计算各分类器对两组被试的分类准确度、灵敏度和特异度。结果: 警示行为DQ同时在5个分类器中作为第一个特征被选中，且在使用警示行为DQ单个特征时，分类准确度达到78.90%；当警示行为DQ与警示行为智龄、大运动智龄和语言能力智龄协同作用时，最高分类准确度为86.71%。结论: ML结合儿心量表能有效区分ASD和GDD儿童；警示行为DQ在ML中起重要作用，而与其他特征联合能提高分类的准确度，对临床高效、准确鉴别ASD和GDD儿童有一定的提示意义和参考价值。.
Keywords: Autism spectrum disorder; Child; Children Neuropsychological and Behavioral Scale-Revision 2016; Global developmental delay; Machine learning.