Using neuroimaging to predict relapse in stimulant dependence: A comparison of linear and machine learning models

OBJECTIVE: Relapse rates are consistently high for stimulant user disorders. In order to obtain prognostic information about individuals in treatment, machine learning models have been applied to neuroimaging and clinical data. Yet few efforts have been made to test these models in independent sampl...

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Detalles Bibliográficos
Autores principales: Gowin, Joshua L., Ernst, Monique, Ball, Tali, May, April C., Sloan, Matthew E., Tapert, Susan F., Paulus, Martin P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Regular Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350259/
https://www.ncbi.nlm.nih.gov/pubmed/30665102
http://dx.doi.org/10.1016/j.nicl.2019.101676

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350259/
https://www.ncbi.nlm.nih.gov/pubmed/30665102
http://dx.doi.org/10.1016/j.nicl.2019.101676

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