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Alcoholism

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Cathodal Transcranial Direct Current Stimulation Does Not Change Implicit Associations Against Alcohol in Alcohol Use Disorder: A Preregistered Clinical Trial.

Addiction biology
Addictive behaviour is shaped by the dynamic interaction of implicit, bottom-up and explicit, top-down cognitive processes. In alcohol use disorder (AUD), implicit alcohol-related associations have been shown to predict increased subsequent alcohol c...

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...

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...

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...

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...

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...

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.

Journal of affective disorders
BACKGROUND: Early diagnosis and treatment of mental illnesses is hampered by the lack of reliable markers. This study used machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder...

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...

Identifying responders to gabapentin for the treatment of alcohol use disorder: an exploratory machine learning approach.

Alcohol and alcoholism (Oxford, Oxfordshire)
BACKGROUND: Gabapentin, an anticonvulsant medication, has been proposed as a treatment for alcohol use disorder (AUD). A multisite study tested gabapentin enacarbil extended-release (GE-XR; 600 mg/twice a day), a prodrug formulation, combined with a ...