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

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 Collabora...

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Detalles Bibliográficos
Autores principales: Kinreich, Sivan, McCutcheon, Vivia V., Aliev, Fazil, Meyers, Jacquelyn L., Kamarajan, Chella, Pandey, Ashwini K., Chorlian, David B., Zhang, Jian, Kuang, Weipeng, Pandey, Gayathri, Viteri, Stacey Subbie-Saenz de., Francis, Meredith W., Chan, Grace, Bourdon, Jessica L., Dick, Danielle M., Anokhin, Andrey P., Bauer, Lance, Hesselbrock, Victor, Schuckit, Marc A., Nurnberger, John I., Foroud, Tatiana M., Salvatore, Jessica E., Bucholz, Kathleen K., Porjesz, Bernice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7960734/
https://www.ncbi.nlm.nih.gov/pubmed/33723218
http://dx.doi.org/10.1038/s41398-021-01281-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7960734/
https://www.ncbi.nlm.nih.gov/pubmed/33723218
http://dx.doi.org/10.1038/s41398-021-01281-2

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