Goh Patrick K, Elkins Anjeli R, Bansal Pevitr S, Eng Ashley G, Martel Michelle M
Department of Psychology, University of Hawai'i at Mānoa, 2530 Dole Street, Sakamaki C400, Honolulu, HI, 96822-2294, USA.
Department of Psychology, University of Kentucky, Lexington, USA.
Res Child Adolesc Psychopathol. 2023 May;51(5):679-691. doi: 10.1007/s10802-023-01022-7. Epub 2023 Jan 19.
Current diagnostic criteria for ADHD include several symptoms that highly overlap in conceptual meaning and interpretation. Additionally, inadequate sensitivity and specificity of current screening tools have hampered clinicians' ability to identify those at risk for related outcomes. Using machine learning techniques, the current study aimed to propose a novel algorithm incorporating key ADHD symptoms to predict concurrent and future (i.e., five years later) ADHD diagnosis and related impairment levels. Participants were 399 children with and without ADHD; multiple informant measures of ADHD symptoms, global impairment, academic performance, and social skills were included as part of an accelerated longitudinal design. Results suggested eight symptoms as most important in predicting impairment outcomes five years later: (1) Has difficulty sustaining attention in tasks or play activities, (2) Does not follow through on instructions and fails to finish work, (3) Has difficulty organizing tasks and activities, (4) Avoids tasks (e.g., schoolwork, homework) that require sustained mental effort, (5) Is often easily distracted, (6) Is often forgetful in daily activities, (7) Fidgets with hands or feet or squirms in seat, and (8) Interrupts/intrudes on others. The algorithm comprising this abbreviated list of symptoms performed just as well as or significantly better than one comprising all 18 symptoms in predicting future global impairment and academic performance, but not social skills. It also predicted concurrent and future ADHD diagnosis with 81-93% accuracy. Continued development of screening tools will be key to ensuring access to clinical services for youth at risk for ADHD.
当前多动症的诊断标准包括几个在概念意义和解释上高度重叠的症状。此外，当前筛查工具的敏感性和特异性不足，阻碍了临床医生识别有相关后果风险者的能力。本研究运用机器学习技术，旨在提出一种纳入多动症关键症状的新算法，以预测当前及未来（即五年后）的多动症诊断及相关损害水平。研究参与者为399名患有和未患多动症的儿童；作为加速纵向设计的一部分，纳入了多动症症状、总体损害、学业成绩和社交技能的多渠道测量数据。结果表明，有八个症状对预测五年后的损害结果最为重要：（1）在任务或游戏活动中难以维持注意力；（2）不遵循指令且无法完成工作；（3）难以组织任务和活动；（4）回避需要持续脑力劳动的任务（如学校作业、家庭作业）；（5）经常容易分心；（6）在日常活动中经常健忘；（7）手脚不停地动或在座位上扭动；（8）打断/侵扰他人。在预测未来总体损害和学业成绩方面，包含这一简短症状列表的算法表现与包含所有18个症状的算法相当，或显著优于后者，但在预测社交技能方面并非如此。该算法预测当前及未来多动症诊断的准确率为81%-93%。持续开发筛查工具将是确保有患多动症风险的青少年能够获得临床服务的关键。
