分析ADHD症状和评估方法的多层相似性方法,考虑DSM-5诊断标准
Syeda Aneela Zahra Shamsi1, Zamir Hussain1, Mehwish Zaman2
1School of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences & Technology (NUST), Islamabad, Pakistan.
Frontiers in psychiatry
|February 11, 2026
概括
这项研究开发了一条管道,以评估注意力缺陷-多动症障碍 (ADHD) 查工具的DSM-5对齐. 该管道准确地识别ADHD症状并评估工具可靠性,有助于改善临床应用.
科学领域:
- 神经发育条件 神经发育条件
- 精神病诊断诊断精神病学诊断的方法
- 机器学习在医疗保健中的应用
背景情况:
- 注意缺陷多动性障碍 (ADHD) 是一种神经发育状况,具有注意力不集中和多动性/冲动性领域.
- 在ADHD领域内的症状重叠使诊断实用性和查工具评估复杂化.
- 现有的ADHD查工具在项目内容和重点方面表现出显著的异质性.
研究的目的:
- 使用相似性框架调查ADHD的域内症状冗余性.
- 开发和验证一个机器学习管道,以评估ADHD查工具与DSM-5标准的一致性.
- 为ADHD查工具提供全面心理测量验证的实用替代方案.
主要方法:
- 采用三层相似性框架,以为基础的权重来评估症状冗余性.
- 开发了一个多阶段的分类管道,其中包含过层和机器学习分类器 (随机森林,SVM,物流回归).
- 在DSM-5的ADHD/非ADHD数据上训练和测试管道,并使用ADHD评分表,SNAP-IV和M-CHAT进行验证.
主要成果:
- 在特定的注意力缺失症状对之间确定了中度的重叠 (相似度分数为0.62和0.58).
- 过层在隔离ADHD症状方面达到97%的准确性.
- 随机森林分类器表现出高性能 (92%的准确性,83%的精度,100%的回忆).
- 验证证实了管道能够评估查工具对齐并排除非ADHD症状的能力.
结论:
- 开发的管道有效地分析了ADHD查工具的优点和局限性.
- 这种方法促进了改进,以提高临床实践中查工具的可靠性和有效性.
- 该管道提供了一种评估诊断工具与DSM-5等既定标准保持一致的实用方法.
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