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对注意力缺陷多动症诊断支持模型的有效性:一种机器学习方法

Kuo-Chung Chu^1,2, Hsin-Jou Huang¹, Yu-Shu Huang^3,4

¹Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei 112, Taiwan.

Journal of personalized medicine

|November 25, 2023

概括

机器学习模型可以帮助早期诊断注意力缺陷多动症障碍 (ADHD). 一个分类和回归树 (CART) 模型显示了ADHD查的卓越性能.

科学领域:

神经科学是一个神经科学.
医疗信息学医疗信息学

背景情况:

早期诊断注意力缺陷多动症 (ADHD) 对于改善患者的治疗结果和降低医疗保健成本至关重要.
开发有效的查工具对于及时干预至关重要.

研究的目的:

开发和评估用于早期诊断ADHD的机器学习模型.
为了比较逻辑回归,分类和回归树 (CART) 和神经网络模型在ADHD查中的性能.

主要方法:

开发了三种机器学习模型 (逻辑回归,CART,神经网络) 用于ADHD诊断.
模型性能使用接收机操作特征 (ROC) 分析进行评估.
对于每个模型,都计算出了灵敏度和特异性.

主要成果:

卡特模型实现了ROC曲线下的最高面积 (0.848),超过了后勤回归 (0.826) 和神经网络 (0.67).
在CART模型中,ADHD诊断的灵敏度为78.8%,特异性为50%.
参与者招生包括ADHD组的74个人和对照组的21个人.

结论:

卡特模型显示了作为ADHD诊断支持工具的巨大潜力.
这种机器学习方法可以扩展到其他神经系统疾病,如自闭症谱系障碍,图雷特综合征和痴呆症.
开发的模型为未来的神经科学研究和临床应用提供了实际价值.

关键词:

注意力缺陷多动性障碍注意力缺陷多动性障碍临床诊断支持临床诊断支持机器学习是机器学习.接收器的运行特征曲线.

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