AIMC Topic: Alcoholism
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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
Feb 25, 2025
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
Feb 18, 2025
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
Dec 20, 2024
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
Nov 11, 2024
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
Sep 13, 2024
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
Aug 22, 2024
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
May 22, 2024
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...
Analysis of addiction craving onset through natural language processing of the online forum Reddit.
PloS one
May 20, 2024
AIMS: Alcohol cravings are considered a major factor in relapse among individuals with alcohol use disorder (AUD). This study aims to investigate the frequency and triggers of cravings in the daily lives of people with alcohol-related issues. Large a...
Insights into ALD and AUD diagnosis and prognosis: Exploring AI and multimodal data streams.
Hepatology (Baltimore, Md.)
May 14, 2024
The rapid evolution of artificial intelligence and the widespread embrace of digital technologies have ushered in a new era of clinical research and practice in hepatology. Although its potential is far from realization, these significant strides hav...
Prediction of adverse events risk in patients with comorbid post-traumatic stress disorder and alcohol use disorder using electronic medical records by deep learning models.
Drug and alcohol dependence
Jan 9, 2024
BACKGROUND: Identifying co-occurring mental disorders and elevated risk is vital for optimization of healthcare processes. In this study, we will use DeepBiomarker2, an updated version of our deep learning model to predict the adverse events among pa...
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