AIMC Topic: Alcoholism

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Staying Relevant in the Digital Age: Exploring the Evolving Frontier of Telehealth for Mental Health in the Military Health System and Veterans Health Administration.

Current psychiatry reports
PURPOSE OF REVIEW: This review examines recent evidence for the effectiveness of telehealth in treating Post-Traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD), alcohol use disorder, and insomnia in military veterans and active-duty se...

High-resolution mapping of alcohol-related brain connectivity in adults using 7T fMRI and multivoxel pattern classification.

Psychiatry research. Neuroimaging
BACKGROUND: Emerging evidence suggests that alcohol use disrupts large-scale brain network interactions, particularly within the triple network model-comprising the Salience Network (SN), Default Mode Network (DMN), and Frontoparietal Network (FPN). ...

Predicting treatment-seeking status for alcohol use disorder using polygenic scores and machine learning in a deeply-phenotyped sample.

Drug and alcohol dependence
BACKGROUND: Few individuals with alcohol use disorder (AUD) receive treatment. Previous studies have shown drinking behavior, psychological problems, and substance dependence to predict treatment seeking. However, to date, no studies have incorporate...

Finding purpose: Integrated latent profile and machine learning analyses identify purpose in life as an important predictor of high-functioning recovery after alcohol treatment.

Addictive behaviors
BACKGROUND: Recent investigations of recovery from alcohol use disorder (AUD) have distinguished subgroups of high and low functioning recovery in data from randomized controlled trials of behavioral treatments for AUD. Analyses considered various in...

Explaining electroencephalogram channel and subband sensitivity for alcoholism detection.

Computers in biology and medicine
Alcoholism, a progressive loss of control over alcohol consumption, deteriorates mental and physical health over time. Automatic alcoholism detection can aid in early interventions and timely corrective actions. For this purpose, electroencephalogram...

Effectiveness of Machine Learning-Based Adjustments to an eHealth Intervention Targeting Mild Alcohol Use.

European addiction research
INTRODUCTION: This study aimed to evaluate effects of three machine learning based adjustments made to an eHealth intervention for mild alcohol use disorder, regarding (a) early dropout, (b) participation duration, and (c) success in reaching persona...

Predicting the Risk of Driving Under the Influence of Alcohol Using EEG-Based Machine Learning.

Computers in biology and medicine
Driving under the influence of alcohol (DUIA) is closely associated with alcohol use disorder (AUD). Our previous study on machine learning (ML) algorithms revealed a very high accuracy of decision trees with neuropsychological features in predicting...

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms.

Scientific reports
The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approache...

Machine learning models for temporally precise lapse prediction in alcohol use disorder.

Journal of psychopathology and clinical science
We developed three machine learning models that predict hour-by-hour probabilities of a future lapse back to alcohol use with increasing temporal precision (i.e., lapses in the next week, next day, and next hour). Model features were based on raw sco...

Person-specific and pooled prediction models for binge eating, alcohol use and binge drinking in bulimia nervosa and alcohol use disorder.

Psychological medicine
BACKGROUND: Machine learning could predict binge behavior and help develop treatments for bulimia nervosa (BN) and alcohol use disorder (AUD). Therefore, this study evaluates person-specific and pooled prediction models for binge eating (BE), alcohol...