Post-traumatic stress comorbidity in substance use disorder: machine learning analyses of phenotypic drivers.
作者信息
Houghton David C, Spratt Heidi M
机构信息
Center for Addiction Sciences and Therapeutics, University of Texas Medical Branch, Galveston, TX, USA.
Department of Psychiatry and Behavioral Sciences, University of Texas Medical Branch, Galveston, TX, USA.
出版信息
Neuropsychopharmacology. 2024 Nov;50(1):345-346. doi: 10.1038/s41386-024-01969-1.
Abstract
摘要
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本文引用的文献
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Neuroscience-informed classification of prevention interventions in substance use disorders: An RDoC-based approach.基于 RDoC 的方法：神经科学信息指导物质使用障碍预防干预的分类。
Neurosci Biobehav Rev. 2024 Apr;159:105578. doi: 10.1016/j.neubiorev.2024.105578. Epub 2024 Feb 13.
2
Behavioral and neurocognitive factors distinguishing post-traumatic stress comorbidity in substance use disorders.区分物质使用障碍并发创伤后应激障碍的行为和神经认知因素。
Transl Psychiatry. 2023 Sep 14;13(1):296. doi: 10.1038/s41398-023-02591-3.
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Development and Feasibility Study of an Addiction-Focused Phenotyping Assessment Battery.成瘾焦点表型评估工具包的开发与可行性研究。
Am J Addict. 2021 Jul;30(4):398-405. doi: 10.1111/ajad.13170. Epub 2021 Apr 28.
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Machine-learning approaches to substance-abuse research: emerging trends and their implications.机器学习方法在药物滥用研究中的应用：新兴趋势及其影响。
Curr Opin Psychiatry. 2020 Jul;33(4):334-342. doi: 10.1097/YCO.0000000000000611.
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Using neuroimaging to predict relapse in stimulant dependence: A comparison of linear and machine learning models.使用神经影像学预测兴奋剂依赖的复发：线性和机器学习模型的比较。
Neuroimage Clin. 2019;21:101676. doi: 10.1016/j.nicl.2019.101676. Epub 2019 Jan 11.
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Biomarkers in substance use disorders.物质使用障碍中的生物标志物。
ACS Chem Neurosci. 2015 Apr 15;6(4):522-5. doi: 10.1021/acschemneuro.5b00067. Epub 2015 Mar 18.
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