Attention deficit hyperactivity disorder is one of the most prevalent neurodevelopmental disorders worldwide. Recent studies show that machine learning has great potential for the diagnosis of attention deficit hyperactivity disorder. The aim of the present article is to systematically review the scientific literature on machine learning studies for the diagnosis of attention deficit hyperactivity disorder, focusing on psychometric questionnaire tools. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were adopted. The review protocol was registered in the PROSPERO database. A search was conducted in three databases—Web of Science Core Collection, Scopus and Pubmed—with the aim of identifying studies that apply ML techniques to support the diagnosis of attention deficit hyperactivity disorder. A total of 17 empirical studies were found that met the established inclusion criteria. The results showed that machine learning can be used to increase the accuracy of attention deficit hyperactivity disorder diagnosis. Machine learning techniques are useful and effective strategies that can complement traditional diagnostics in patients with attention deficit hyperactivity disorder.

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机器学习模型在注意力缺陷多动障碍心理测量问卷中应用的系统评价

注意力缺陷多动障碍是全世界最常见的神经发育障碍之一。最近的研究表明，机器学习在诊断注意力缺陷多动障碍方面具有巨大潜力。本文的目的是系统回顾用于诊断注意力缺陷多动障碍的机器学习研究的科学文献，重点关注心理测量问卷工具。系统评价和荟萃分析的首选报告项目 (PRISMA) 指南获得通过。审查方案已在 PROSPERO 数据库中注册。在三个数据库（Web of Science Core Collection、Scopus 和 Pubmed）中进行了搜索，目的是确定应用机器学习技术来支持注意力缺陷多动障碍诊断的研究。总共发现 17 项实证研究符合既定的纳入标准。结果表明，机器学习可用于提高注意力缺陷多动障碍诊断的准确性。机器学习技术是有用且有效的策略，可以补充注意力缺陷多动障碍患者的传统诊断方法。