背景:儿童和青少年精神病理学的诊断涉及多方面的方法,整合临床观察,行为评估,病史,认知测试,和家庭背景信息。数字技术,特别是基于互联网的平台,用于管理护理人员评级的问卷,越来越多地用于这一领域,特别是在筛选阶段。数据收集数字平台的兴起推动了先进的精神病理学分类方法,如监督机器学习(ML),进入研究和临床环境的前沿。这个转变,最近被称为心理信息学,已通过逐步将计算设备纳入临床工作流程来促进。然而,远程医疗和ML方法之间的实际整合尚未实现。
目标:在这些前提下,探索ML应用分析数字化收集数据的潜力,可能对支持早期精神病理学诊断的临床实践具有重要意义.这项研究的目的是,因此,使用基于互联网的父母报告的社会记忆数据,利用ML模型对注意力缺陷/多动障碍(ADHD)和自闭症谱系障碍(ASD)进行分类,旨在为新的求助家庭获得准确的预测模型。
方法:在本回顾性研究中,单中心观察研究,我们收集了1688名因疑似神经发育疾病而转诊的儿童和青少年的社会记忆数据.数据包括社会人口统计学,临床,环境,和发展因素,通过第一个基于互联网的意大利神经发育障碍筛查工具远程收集,美狄亚信息和临床评估在线(MedicalBIT)。随机森林(RF),决策树,并使用分类精度开发和评估逻辑回归模型,灵敏度,特异性,以及自变量的重要性。
结果:RF模型显示出稳健的准确性,ADHD达到84%(95%CI82-85;P<.001),ASD分类达到86%(95%CI84-87;P<.001)。敏感度也很高,93%的ADHD和95%的ASD。相比之下,DT和LR模型的精度较低(DT74%,95%CI71-77;ADHD的P<.001;DT79%,95%CI77-82;ASD的P<.001;LR61%,95%CI57-64;多动症P<.001;LR63%,95%CI60-67;ASD的P<.001)和敏感性(DT:ADHD为82%,ASD为88%;LR:ADHD为62%,ASD为68%)。考虑分类的自变量在两个模型之间的重要性不同,反映了3ML方法的不同特征。
结论:这项研究强调了ML模型的潜力,特别是RF,加强儿童和青少年精神病理学的诊断过程。总之,当前的发现强调了在诊断过程中利用数字平台和计算技术的重要性.虽然解释性仍然至关重要,开发的方法可能为临床医生提供有价值的筛查工具,强调在诊断过程中嵌入计算技术的重要性。
BACKGROUND: Diagnosis of child and adolescent psychopathologies involves a multifaceted approach, integrating clinical observations, behavioral assessments, medical history, cognitive testing, and familial context information. Digital technologies, especially internet-based platforms for administering caregiver-rated questionnaires, are increasingly used in this field, particularly during the screening phase. The ascent of digital platforms for data collection has propelled advanced psychopathology classification methods such as supervised machine learning (ML) into the forefront of both research and clinical environments. This shift, recently called psycho-informatics, has been facilitated by gradually incorporating computational devices into clinical workflows. However, an actual integration between telemedicine and the ML approach has yet to be fulfilled.
OBJECTIVE: Under these premises, exploring the potential of ML applications for analyzing digitally collected data may have significant implications for supporting the clinical practice of diagnosing early psychopathology. The purpose of this study was, therefore, to exploit ML models for the classification of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) using internet-based parent-reported socio-anamnestic data, aiming at obtaining accurate predictive models for new help-seeking families.
METHODS: In this retrospective, single-center observational study, socio-anamnestic data were collected from 1688 children and adolescents referred for suspected neurodevelopmental conditions. The data included sociodemographic, clinical, environmental, and developmental factors, collected remotely through the first Italian internet-based screening tool for neurodevelopmental disorders, the Medea Information and Clinical Assessment On-Line (MedicalBIT). Random forest (RF), decision tree, and logistic regression models were developed and evaluated using classification accuracy, sensitivity, specificity, and importance of independent variables.
RESULTS: The RF model demonstrated robust accuracy, achieving 84% (95% CI 82-85; P<.001) for ADHD and 86% (95% CI 84-87; P<.001) for ASD classifications. Sensitivities were also high, with 93% for ADHD and 95% for ASD. In contrast, the DT and LR models exhibited lower accuracy (DT 74%, 95% CI 71-77; P<.001 for ADHD; DT 79%, 95% CI 77-82; P<.001 for ASD; LR 61%, 95% CI 57-64; P<.001 for ADHD; LR 63%, 95% CI 60-67; P<.001 for ASD) and sensitivities (DT: 82% for ADHD and 88% for ASD; LR: 62% for ADHD and 68% for ASD). The independent variables considered for classification differed in importance between the 2 models, reflecting the distinct characteristics of the 3 ML approaches.
CONCLUSIONS: This study highlights the potential of ML models, particularly RF, in enhancing the diagnostic process of child and adolescent psychopathology. Altogether, the current findings underscore the significance of leveraging digital platforms and computational techniques in the diagnostic process. While interpretability remains crucial, the developed approach might provide valuable screening tools for clinicians, highlighting the significance of embedding computational techniques in the diagnostic process.