Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach

Abstract Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the...

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Detalhes bibliográficos

Principais autores:	Sivan Kinreich, Vivia V. McCutcheon, Fazil Aliev, Jacquelyn L. Meyers, Chella Kamarajan, Ashwini K. Pandey, David B. Chorlian, Jian Zhang, Weipeng Kuang, Gayathri Pandey, Stacey Subbie-Saenz de. Viteri, Meredith W. Francis, Grace Chan, Jessica L. Bourdon, Danielle M. Dick, Andrey P. Anokhin, Lance Bauer, Victor Hesselbrock, Marc A. Schuckit, John I. Nurnberger, Tatiana M. Foroud, Jessica E. Salvatore, Kathleen K. Bucholz, Bernice Porjesz
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Nature Publishing Group 2021-03-01
coleção:	Translational Psychiatry
Acesso em linha:	https://doi.org/10.1038/s41398-021-01281-2
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