Genomic conditions can be associated with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presentation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND-GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question. A total of 493 individuals were included: 389 with a ND-GC, mean age = 9.01, 66% male) and 104 siblings without known genomic conditions (controls, mean age = 10.23, 53% male). Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status and identified limited sets of variables that gave the best classification performance. Exploratory graph analysis was used to understand associations within the final variable set. All machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0.915). We identified a subset of 30 variables best discriminating between individuals with ND-GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development. This study used cross-sectional data from a cohort study which was imbalanced with respect to ND-GC status. Our model requires validation in independent datasets and with longitudinal follow-up data for validation before clinical application. In this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND-GC from controls and highlight higher-order structure within these measures. This work is a step towards developing a screening instrument to identify young people with ND-GCs who might benefit from further specialist assessment.

中文翻译：

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识别与智力障碍相关的基因组疾病的神经发育和精神病学特征：一种机器学习方法

基因组状况可能与发育迟缓、智力障碍、自闭症谱系障碍以及身心健康症状有关。它们个体罕见且表现高度可变，这限制了标准临床指南在诊断和治疗中的使用。一个简单的筛选工具可以识别患有与神经发育障碍 (ND-GC) 相关的基因组疾病的年轻人，他们可以从进一步的支持中受益，这将具有相当大的价值。我们使用机器学习方法来解决这个问题。总共包括 493 人：389 人患有 ND-GC，平均年龄 = 9.01，66% 男性）和 104 名没有已知基因组状况的兄弟姐妹（对照，平均年龄 = 10.23，53% 男性）。初级照顾者完成了行为评估，神经发育和精神症状以及身体健康和发育。机器学习技术（惩罚逻辑回归、随机森林、支持向量机和人工神经网络）被用于开发 ND-GC 状态的分类器，并确定了提供最佳分类性能的有限变量集。探索性图表分析用于理解最终变量集中的关联。所有机器学习方法都识别出具有高分类准确度的变量集（AUROC 在 0.883 和 0.915 之间）。我们确定了 30 个变量的子集，这些变量最能区分 ND-GCs 个体和对照，形成 5 个维度：行为、分离焦虑、情境焦虑、交流和运动发育。本研究使用了一项队列研究的横断面数据，该研究在 ND-GC 状态方面不平衡。我们的模型需要在独立数据集中进行验证，并在临床应用前使用纵向随访数据进行验证。在这项研究中，我们开发了模型，这些模型确定了一组紧凑的精神和身体健康措施，这些措施将 ND-GC 患者与对照组区分开来，并突出了这些措施中的高阶结构。这项工作是朝着开发一种筛查工具迈出的一步，该工具可识别患有 ND-GC 的年轻人，他们可能会从进一步的专家评估中受益。我们开发的模型确定了一组紧凑的精神和身体健康措施，这些措施将 ND-GC 患者与对照组区分开来，并突出显示这些措施中的高阶结构。这项工作是朝着开发一种筛查工具迈出的一步，该工具可识别患有 ND-GC 的年轻人，他们可能会从进一步的专家评估中受益。我们开发的模型确定了一组紧凑的精神和身体健康措施，这些措施将具有 ND-GC 的个体与对照组区分开来，并突出显示这些措施中的高阶结构。这项工作是朝着开发一种筛查工具迈出的一步，该工具可识别患有 ND-GC 的年轻人，他们可能会从进一步的专家评估中受益。