Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features

Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (age...

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Bibliografiske detaljer

Principais autores:	Chella Kamarajan, Ashwini K. Pandey, David B. Chorlian, Jacquelyn L. Meyers, Sivan Kinreich, Gayathri Pandey, Stacey Subbie-Saenz de Viteri, Jian Zhang, Weipeng Kuang, Peter B. Barr, Fazil Aliev, Andrey P. Anokhin, Martin H. Plawecki, Samuel Kuperman, Laura Almasy, Alison Merikangas, Sarah J. Brislin, Lance Bauer, Victor Hesselbrock, Grace Chan, John Kramer, Dongbing Lai, Sarah Hartz, Laura J. Bierut, Vivia V. McCutcheon, Kathleen K. Bucholz, Danielle M. Dick, Marc A. Schuckit, Howard J. Edenberg, Bernice Porjesz
Format:	Artigo
Sprog:	Inglês
Udgivet:	MDPI AG 2023-05-01
Serier:	Behavioral Sciences
Fag:	alcohol use disorder (AUD) EEG source functional connectivity default mode network alcohol-related memory problems random forests
Online adgang:	https://www.mdpi.com/2076-328X/13/5/427
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