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通过人工智能预测儿童和青少年的ADHD:对常见模型的全面审查

Arefeh Ameri¹, Farzad Salmanizadeh¹, Hamidreza Samzadeh Kermani²

¹Medical Informatics Research Center, Institute for Futures Studies in Health Kerman University of Medical Sciences Kerman Iran.

Health science reports

|December 24, 2025

概括

人工智能 (AI) 对年轻人早期注意力缺陷/多动障碍 (ADHD) 预测有希望. 机器学习模型的准确度超过80%,有助于早期诊断和改善结果.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
儿科儿科儿科

背景情况:

注意缺陷/多动障碍 (ADHD) 是儿童和青少年常见的一种神经发育障碍.
早期诊断ADHD对于缓解长期的认知和行为问题至关重要.
人工智能 (AI) 通过数据分析为早期ADHD检测提供了潜力.

研究的目的:

审查最近关于人工智能的应用在儿童和青少年预测ADHD的研究.
确定用于ADHD预测的最有效的AI方法和数据类型.

主要方法:

在PubMed,Scopus,Web of Science和Embase进行了系统的文献搜索,截至2025年10月14日.
数据提取遵循PRISMA-ScR指导方针,结果使用图表和表格呈现.
在选了3981个记录后,包括了42项研究.

主要成果:

随机森林 (RF) 和支持向量机器 (SVM) 是最常见的AI方法,在ADHD中平均预测准确度超过80%.
基于问卷的数据 (27.2%) 和人口信息 (19.4%) 是最常用的数据类型.
行为认知特征也是重要的数据来源 (15.5%).

结论:

人工智能,特别是像RF这样的机器学习模型,在年轻人中预测和管理ADHD方面表现出巨大的潜力.
人工智能工具可以支持早期的ADHD诊断,潜在地改善认知,行为和教育成果.
临床实施需要解决可解释性,工作流集成和安全和实际使用的伦理考虑.

关键词:

人工智能的人工智能是人工智能.注意缺陷/多动障碍 (ADHD) 是一种注意缺陷/多动障碍.机器学习是机器学习.随机的森林随机的森林

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