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

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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Nature Publishing Group 2021-03-01
Series:Translational Psychiatry
Online Access:https://doi.org/10.1038/s41398-021-01281-2

Internet

https://doi.org/10.1038/s41398-021-01281-2

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