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在使用机器学习的ADHD评估中检测不可信的症状

John-Christopher A Finley¹, Matthew S Phillips², Jason R Soble^2,3

¹Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Journal of clinical and experimental neuropsychology

|January 25, 2025

概括

无监督机器学习 (ML) 可以有效地检测在接受注意力缺陷/多动症障碍 (ADHD) 评估的成年人中不可信的症状报告. 这种新的方法有助于通过识别夸张或伪造的症状来提高诊断的准确性.

科学领域:

精神病学和心理学精神病学和心理学
医疗保健中的人工智能
临床评估临床评估

背景情况:

对注意力缺陷/多动障碍 (ADHD) 的诊断评估因症状捏造或夸大而变得越来越复杂.
需要新的方法来改善成人ADHD评估中不可信的症状的检测.

研究的目的:

研究无监督机器学习 (ML) 在成年ADHD评估期间检测不可信的症状报告中的实用性.
评估ML是否可以识别那些捏造或夸大ADHD症状的个体.

主要方法:

无监督的ML模型 (sidClustering) 应用于623名接受ADHD评估的成年人的症状有效性测试得分.
该模型从自我报告问卷中合成了原始分数,而没有使用预先确定的截止值.
将ML衍生组与可信与不可信的症状报告的既定评级进行了比较.

主要成果:

ML模型成功地确定了两个与可信和不可信的症状报告有显著关联的不同组.
模型的性能是一致的,不管用于定义不可靠报告的有效性测试提升的数量.
一般精神病症状的有效性测试是最有影响力的,其次是ADHD特异性症状有效性测试.

结论:

无监督的ML可以有效地识别ADHD评估中的不可信的症状报告,使用没有切断的症状有效性测试得分.
这些发现支持使用两个有效性测试升级来识别不可信的报告.
无监督的ML显示为增强ADHD诊断评估的准确性和效率的补充工具.

关键词:

更多关于 ADHD ADHD 的文章机器学习机器学习人工智能的人工智能是人工智能.马林格勒是什么意思马林格勒是什么意思症状的有效性症状的有效性

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