Jaiswal Aditi, Wall Dennis P, Washington Peter
Information and Computer Sciences Department, University of Hawaii at Manoa, Honolulu, USA.
Departments of Pediatrics & Biomedical Data Science, Stanford University, Stanford, USA.
IEEE EMBS Int Conf Biomed Health Inform. 2024 Nov;2024. doi: 10.1109/bhi62660.2024.10913850.
Autism and Attention-Deficit Hyperactivity Disorder (ADHD) are two of the most commonly observed neurodevelopmental conditions in childhood. Providing a specific computational assessment to distinguish between the two can prove difficult and time intensive. Given the high prevalence of their co-occurrence, there is a need for scalable and accessible methods for distinguishing the co-occurrence of autism and ADHD from individual diagnoses. The first step is to identify a core set of features that can serve as the basis for behavioral feature extraction. We trained machine learning models on data from the National Survey of Children's Health to identify behaviors to target as features in automated clinical decision support systems. A model trained on the binary task of distinguishing either developmental delay (autism or ADHD) vs. neither achieved sensitivity >92% and specificity >94%, while a model trained on the 4-way classification task of autism vs. ADHD vs. both vs. none demonstrated >65% sensitivity and >66% specificity. While the performance of the binary model was respectable, the relatively low performance in the differential classification of autism and ADHD highlights the challenges that persist in achieving specificity within clinical decision support tools for developmental delays. Nevertheless, this study demonstrates the potential of applying behavioral questionnaires not traditionally used for clinical purposes towards supporting digital screening assessments for pediatric developmental delays.
自闭症和注意力缺陷多动障碍(ADHD)是儿童期最常见的两种神经发育疾病。提供一种特定的计算评估方法来区分这两种疾病可能既困难又耗时。鉴于它们共病的高发生率,需要有可扩展且易于使用的方法来区分自闭症和ADHD的共病情况与个体诊断。第一步是确定一组核心特征,作为行为特征提取的基础。我们利用全国儿童健康调查的数据训练机器学习模型,以确定在自动化临床决策支持系统中可作为特征的目标行为。在区分发育迟缓(自闭症或ADHD)与非发育迟缓的二元任务上训练的模型,其灵敏度>92%,特异性>94%;而在自闭症与ADHD与两者都有与两者都无的四分类任务上训练的模型,其灵敏度>65%,特异性>66%。虽然二元模型的性能不错,但自闭症和ADHD的差异分类中相对较低的性能凸显了在发育迟缓的临床决策支持工具中实现特异性所面临的持续挑战。尽管如此,这项研究证明了将传统上不用于临床目的的行为问卷应用于支持儿科发育迟缓数字筛查评估的潜力。