勘误：使用有效连接性和图论方法检测社交焦虑障碍的机器学习
Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.
作者信息
Al-Ezzi Abdulhakim, Kamel Nidal, Al-Shargabi Amal A, Al-Shargie Fares, Al-Shargabi Alaa, Yahya Norashikin, Al-Hiyali Mohammed Isam
机构信息
Centre for Intelligent Signal & Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia.
College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam.
出版信息
Front Psychiatry. 2023 Jul 24;14:1257713. doi: 10.3389/fpsyt.2023.1257713. eCollection 2023.
Abstract
[This corrects the article DOI: 10.3389/fpsyt.2023.1155812.].
摘要
[本文更正了文章的数字对象标识符：10.3389/fpsyt.2023.1155812。]
相似文献
1
Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.勘误：使用有效连接性和图论方法检测社交焦虑障碍的机器学习
Front Psychiatry. 2023 Jul 24;14:1257713. doi: 10.3389/fpsyt.2023.1257713. eCollection 2023.
2
Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.使用有效连接性和图论测量方法进行社交焦虑障碍检测的机器学习
Front Psychiatry. 2023 May 9;14:1155812. doi: 10.3389/fpsyt.2023.1155812. eCollection 2023.
3
EEG effective connectivity networks for an attentive task requiring vigilance based on dynamic partial directed coherence.基于动态偏相干的用于需要警觉的注意力任务的脑电图有效连接网络。
J Integr Neurosci. 2020 Mar 30;19(1):111-118. doi: 10.31083/j.jin.2020.01.1234.
4
Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach.颞叶癫痫的动态功能连接：图论和机器学习方法。
Neurol Sci. 2021 Jun;42(6):2379-2390. doi: 10.1007/s10072-020-04759-x. Epub 2020 Oct 14.
5
Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation.基于创新 EEG 生物标志物的机器学习在生理衰老方面对阿尔茨海默病的分类。
J Alzheimers Dis. 2020;75(4):1253-1261. doi: 10.3233/JAD-200171.
6
Corrigendum: Abilities and Disabilities-Applying Machine Learning to Disentangle the Role of Intelligence in Diagnosing Autism Spectrum Disorders.勘误：能力与残疾——应用机器学习剖析智力在自闭症谱系障碍诊断中的作用
Front Psychiatry. 2022 Apr 25;13:908350. doi: 10.3389/fpsyt.2022.908350. eCollection 2022.
7
Corrigendum: Enhanced Topological Network Efficiency in Preschool Autism Spectrum Disorder: A Diffusion Tensor Imaging Study.勘误：学龄前自闭症谱系障碍中增强的拓扑网络效率：一项扩散张量成像研究。
Front Psychiatry. 2019 Feb 19;10:68. doi: 10.3389/fpsyt.2019.00068. eCollection 2019.
8
Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.机器学习在预测共病创伤后应激障碍和重度抑郁症患者经颅磁刺激临床反应中的应用：一项静息态脑电图研究。
J Affect Disord. 2019 Jun 1;252:47-54. doi: 10.1016/j.jad.2019.03.077. Epub 2019 Mar 30.
9
Classification of Rajayoga Meditators Based on the Duration of Practice Using Graph Theoretical Measures of Functional Connectivity from Task-Based Functional Magnetic Resonance Imaging.基于任务态功能磁共振成像功能连接的图论测量方法，根据练习时长对胜王瑜伽冥想者进行分类。
Int J Yoga. 2022 May-Aug;15(2):96-105. doi: 10.4103/ijoy.ijoy_17_22. Epub 2022 Sep 5.
10
Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.基于弥散加权神经影像学和图论的支持向量机分类对重度抑郁症的诊断
Front Psychiatry. 2015 Feb 18;6:21. doi: 10.3389/fpsyt.2015.00021. eCollection 2015.
Zotero 插件