AIMC Topic: Alcoholism
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Analysis of Clinical Parameters, Drug Consumption and Use of Health Resources in a Southern European Population with Alcohol Abuse Disorder during COVID-19 Pandemic.
International journal of environmental research and public health
Jan 26, 2022
The disruption in healthcare attention to people with alcohol dependence, along with psychological decompensation as a consequence of lockdown derived from the COVID-19 pandemic could have a negative impact on people who suffer from alcohol abuse dis...
A combination of support vector machine and voxel-based morphometry in adult male alcohol use disorder patients with cognitive deficits.
Brain research
Aug 31, 2021
Cognitive performance deteriorates with drinking. However, the neural basis of cognitive deficits in alcohol use disorder (AUD) is still incompletely understood. Here we examined the relationship between overall drinking, brain structural alterations...
Machine learning prediction of dropping out of outpatients with alcohol use disorders.
PloS one
Aug 2, 2021
BACKGROUND: Alcohol use disorder (AUD) is a chronic disease with a higher recurrence rate than that of other mental illnesses. Moreover, it requires continuous outpatient treatment for the patient to maintain abstinence. However, with a low probabili...
Discrimination of alcohol dependence based on the convolutional neural network.
PloS one
Oct 27, 2020
In this paper, a total of 20 sites of single nucleotide polymorphisms (SNPs) on the serotonin 3 receptor A gene (HTR3A) and B gene (HTR3B) are used for feature fusion with age, education and marital status information, and the grid search-support vec...
Predicting alcohol dependence from multi-site brain structural measures.
Human brain mapping
Oct 16, 2020
To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored ...
A machine learning approach to risk assessment for alcohol withdrawal syndrome.
European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
May 14, 2020
At present, risk assessment for alcohol withdrawal syndrome relies on clinical judgment. Our aim was to develop accurate machine learning tools to predict alcohol withdrawal outcomes at the individual subject level using information easily attainable...
Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus 'trained' machine learning models.
Addiction (Abingdon, England)
Mar 26, 2020
BACKGROUND AND AIMS: Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML al...
Model to Predict Progression of Liver Disease in Heavy Drinkers Is Useful Today and Supports the Future of Deep Learning.
Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
Feb 13, 2020
Validation of an alcohol misuse classifier in hospitalized patients.
Alcohol (Fayetteville, N.Y.)
Sep 28, 2019
BACKGROUND: Current modes of identifying alcohol misuse in hospitalized patients rely on self-report questionnaires and diagnostic codes that have limitations, including low sensitivity. Information in the clinical notes of the electronic health reco...
Salivary microRNAs identified by small RNA sequencing and machine learning as potential biomarkers of alcohol dependence.
Epigenomics
May 29, 2019
Salivary miRNA can be easily accessible biomarkers of alcohol dependence (AD). The miRNA transcriptome in the saliva of 56 African-Americans (AAs; 28 AD patients/28 controls) and 64 European-Americans (EAs; 32 AD patients/32 controls) was profiled ...
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