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机器学习用于ADHD诊断:从家长报告,自我报告和神经心理测量中选择特征

Yun-Wei Dai¹, Chia-Fen Hsu¹

¹Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan 701401, Taiwan.

Children (Basel, Switzerland)

|November 27, 2025

概括

机器学习模型使用家长报告和绩效测试准确识别了注意力缺陷/多动症 (ADHD) 障碍. 关键预测因素包括社会问题和执行功能障碍,改善了客观的ADHD诊断.

科学领域:

神经科学是一个神经科学.
精神病学是一个精神病学.
机器学习机器学习

背景情况:

注意缺陷/多动障碍 (ADHD) 是一种复杂的神经发育状况.
目前的ADHD诊断严重依赖于主观的临床判断.
需要客观,临床适用的诊断工具.

研究的目的:

开发和验证用于ADHD诊断的机器学习模型.
通过使用多信息元数据,识别ADHD的强有力的预测因素.
为了提高ADHD诊断程序的客观性.

主要方法:

应用机器学习 (ML) 技术对255名台湾儿童和青少年的数据.
使用家长报告,自我报告和基于绩效的措施 (持续绩效测试 - CPT).
使用嵌套交叉验证以确保可靠的模型性能估计.

主要成果:

ML模型实现了高的分类准确性 (AUCs ≈0.886-0.906).
父母评价的社会问题,执行功能障碍和自我调节是重要的预测因素.
来自CPT反应时间的Ex-Gaussian参数比原始分数更具信息性.

结论:

整合多信息者评级和基于任务的措施可以提高ADHD诊断.
可解释的ML模型在ADHD评估中显示出临床应用的前景.
客观数据可以在诊断ADHD时补充主观判断.

关键词:

注意力缺陷/多动症障碍.前高斯式的高斯式.执行职能执行职能是指执行职能.功能选择功能选择机器学习是机器学习.嵌套的交叉验证.自我控制,自我控制.

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