Predicting the Path: How Machine Learning Can Identify Subtypes of Epilepsy and Predict Disease Progression.
作者信息
Muldoon Sarah F
机构信息
Department of Mathematics, Institute for Artificial Intelligence and Data Science, Neuroscience Program, University at Buffalo.
出版信息
Epilepsy Curr. 2024 Sep 28;24(6):423-425. doi: 10.1177/15357597241279744. eCollection 2024 Nov-Dec.
Abstract
摘要
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本文引用的文献
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Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images.基于脑影像的机器学习鉴定颞叶癫痫的 4 种生物型。
Nat Commun. 2024 Mar 12;15(1):2221. doi: 10.1038/s41467-024-46629-6.
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Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics.使用机器学习应用程序对形态和功能图论指标进行分析，探讨颞叶癫痫的网络表型及其临床意义。
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Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression.一项全球性 ENIGMA 研究评估了局灶性和全面性癫痫中的结构网络改变，这些改变与癫痫风险基因表达的轴相一致。
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Differential Patterns of Change in Brain Connectivity Resulting from Severe Traumatic Brain Injury.重度创伤性脑损伤导致的脑连接性变化的差异模式。
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Neurobehavioural comorbidities of epilepsy: towards a network-based precision taxonomy.癫痫的神经行为共病：迈向基于网络的精准分类学。
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