Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning...
Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
About this item
Full title
Author / Creator
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. and Porjesz, Bernice
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
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 Collaborative Study on the Genetics of Alcoholism (...
Alternative Titles
Full title
Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
Authors, Artists and Contributors
Author / Creator
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
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_a4b99fa326444960a6af8f5bcbc55d5a
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_a4b99fa326444960a6af8f5bcbc55d5a
Other Identifiers
ISSN
2158-3188
E-ISSN
2158-3188
DOI
10.1038/s41398-021-01281-2
