在患有神经发育障碍的儿童和青少年的认知异质性的跨诊断检查
Sarah Al-Saoud1, Emily S Nichols1,2, Marie Brossard-Racine3
1Applied Psychology, Faculty of Education, Western University, London, Ontario, Canada.
概括
患有神经发育障碍的儿童的认知特征是多样化的,并未被传统诊断定义. 数据驱动的方法揭示了不同的认知子组,突出显示了跨诊断评估的必要性.
科学领域:
- 神经科学是一个神经科学.
- 发展心理学 发展心理学
- 医疗保健中的机器学习
背景情况:
- 患有神经发育障碍 (NDD) 的儿童和青少年表现出显著的认知变异性,目前的诊断类别无法完全解释.
- 传统的诊断系统往往无法捕捉NDD中存在的细微的认知异质性,需要创新的评估方法.
- 提出了一种跨诊断方法,以更好地了解跨不同神经发育条件的认知功能.
研究的目的:
- 在大量患有NDD的儿童和青少年中使用数据驱动的方法识别不同的认知特征.
- 研究这些数据驱动的认知概况与传统的诊断分类 (如ADHD,ASD) 之间的关系.
- 探索无监督机器学习技术在发现认知异质性的有用性.
主要方法:
- 一项回顾性队列研究,涉及1529名患有NDD的7-18岁儿童和青少年.
- 管理一个12任务,基于网络的神经认知电池评估短期记忆,语言能力和推理.
- 无监督机器学习的应用,包括自组织地图 (人工神经网络) 和k-means集群,以识别子组.
主要成果:
- 在样本中确定了六个不同的集群,代表了不同的认知概况.
- 认知特征与传统的诊断类别不一致,这表明有明显的重叠和异质性.
- 这些发现表明,一些典型的发育个体可能有未被诊断的学习困难.
结论:
- 一种跨诊断,数据驱动的方法有效地揭示了NDD儿童和青少年的认知异质性.
- 传统的诊断标签不足以表征患有NDD的个体的认知概况.
- 在教育环境中,急需可访问的认知评估工具,以识别所有人群的学习困难.
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