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Updated: Jan 8, 2026

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
10:02

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

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将可解释的AI与临床特征集成,以增强ADHD诊断理解

Hafiz Muhammad Shakeel1, Grigorios Antoniou1, Marios Adamou2

  • 1School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, United Kingdom.

Frontiers in psychiatry
|December 12, 2025
PubMed
概括

机器学习通过整合各种临床数据,提高准确性和透明度,提高了成年ADHD诊断. 可解释的AI方法验证了诸如ADHD症状和并发症等关键预测因素.

关键词:
更多关于 ADHD ADHD 的文章卡尔斯 (Caars) 是一个城市.迪瓦迪瓦 迪瓦迪瓦可以解释性的解释性.机器学习是机器学习.心理健康 心理健康模型的解释性可解释性

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