高危婴儿的行为和发育测量对 3 岁自闭症的预测：纵向跨领域分类器分析。

Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis.

机构信息

Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Kapittelweg 29, 6525 EN, Nijmegen, The Netherlands.

Centre for Brain and Cognitive Development, Birkbeck, University of London, 32 Torrington Square, London, WC1E 7JL, UK.

出版信息

J Autism Dev Disord. 2018 Jul;48(7):2418-2433. doi: 10.1007/s10803-018-3509-x.

DOI:10.1007/s10803-018-3509-x

PMID:29453709

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5996007/

Abstract

We integrated multiple behavioural and developmental measures from multiple time-points using machine learning to improve early prediction of individual Autism Spectrum Disorder (ASD) outcome. We examined Mullen Scales of Early Learning, Vineland Adaptive Behavior Scales, and early ASD symptoms between 8 and 36 months in high-risk siblings (HR; n = 161) and low-risk controls (LR; n = 71). Longitudinally, LR and HR-Typical showed higher developmental level and functioning, and fewer ASD symptoms than HR-Atypical and HR-ASD. At 8 months, machine learning classified HR-ASD at chance level, and broader atypical development with 69.2% Area Under the Curve (AUC). At 14 months, ASD and broader atypical development were classified with approximately 71% AUC. Thus, prediction of ASD was only possible with moderate accuracy at 14 months.

摘要

我们整合了多个时间点的多项行为和发育指标，并运用机器学习方法来提高对个体自闭症谱系障碍（ASD）结局的早期预测。我们在高风险兄弟姐妹（HR，n=161）和低风险对照组（LR，n=71）中研究了 Mullen 早期学习量表、Vineland 适应行为量表以及 8 至 36 个月时的早期 ASD 症状。从纵向来看，LR 和 HR-typical 的发育水平和功能较高，ASD 症状较少，而 HR-atypical 和 HR-ASD 则较低。在 8 个月时，机器学习对 HR-ASD 的分类仅处于随机水平，对更广泛的非典型发育的分类准确率为 69.2%。在 14 个月时，ASD 和更广泛的非典型发育的分类准确率约为 71%。因此，14 个月时 ASD 的预测准确率仅为中等水平。