Addictions

Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.

Proteomics can be used to assess individual protein abundances, which could reflect genotypic and environmental effects and potentially predict grain/malt quality. In this study, 79 barley grain samples (genotype-location-year combinations) from Californian multi-environment trials (2017-2022) were assessed using liquid chromatography-mass spectrometry. In total, 3104 proteins were identified acro...

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

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 the risk of DUIA despite limited data availability. Thus, this study aimed at comparing six well-known ML algorithms based on electroencephalographic...

Teaching deep networks to see shape: Lessons from a simplified visual world.

Deep neural networks have been remarkably successful as models of the primate visual system. One crucial problem is that they fail to account for the ...

Machine learning integration with response surface methodology to enhance the removal efficacy of arsenate (V) through sulfur-functionalized mxene coated QPPO/PVA AEM.

Arsenic, a poisonous and carcinogenic heavy metal in drinking water, presents severe health risks to humans, including skin lesions, neurological dama...

Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022.

Feature selection is essentially the process of picking informative and relevant features from a larger collection of features. Few studies have focus...

Spatial patterns of rural opioid-related hospital emergency department visits: A machine learning analysis.

As opioid-related overdose emergency department visits continue to rise in the United States, there is a need to understand the location and magnitude...

Factors predicting access to medications for opioid use disorder for housed and unhoused patients: A machine learning approach.

BACKGROUND: Opioid use disorder (OUD) is a growing public health crisis, with opioids involved in an overwhelming majority of drug overdose deaths in ...

Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.

Most operant conditioning circuits predominantly focus on simple feedback process, few studies consider the intricacies of feedback outcomes and the u...

Deep-learning-assisted thermogalvanic hydrogel fiber sensor for self-powered in-nostril respiratory monitoring.

Direct and consistent monitoring of respiratory patterns is crucial for disease prognostication. Although the wired clinical respiratory monitoring ap...

The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review.

BACKGROUND: The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (...

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

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 ...

Global Suicide Mortality Rates (2000-2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data.

Suicide research is directed at understanding social, economic, and biological causes of suicide thoughts and behaviors. (1) Background: Worldwide, ce...

Mesocorticolimbic and Cardiometabolic Diseases-Two Faces of the Same Coin?

The risk behaviors underlying the most prevalent chronic noncommunicable diseases (NCDs) encompass alcohol misuse, unhealthy diets, smoking and sedent...

Classifying High-Risk Patients for Persistent Opioid Use After Major Spine Surgery: A Machine-Learning Approach.

BACKGROUND: Persistent opioid use is a common occurrence after surgery and prolonged exposure to opioids may result in escalation and dependence. The ...

AI and Big Data approaches to addressing the opioid crisis: a scoping review protocol.

INTRODUCTION: This paper outlines the steps necessary to assess the latest developments in artificial intelligence (AI) as well as Big Data technologi...

A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records.

Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This research trained three backpropagation neural networks ...

Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis.

BACKGROUND: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physi...

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

We developed three machine learning models that predict hour-by-hour probabilities of a future lapse back to alcohol use with increasing temporal prec...

Machine Learning-Based Prediction of Binge Drinking among Adults in the United State: Analysis of the 2022 Health Information National Trends Survey.

Little is known about the association of social media and belief in alcohol and cancer with binge drinking. This study aimed to perform feature select...

Predicting routes of phase I and II metabolism based on quantum mechanics and machine learning.

Unexpected metabolism could lead to the failure of many late-stage drug candidates or even the withdrawal of approved drugs. Thus, it is critical to p...