[HTML][HTML] How machine learning is used to study addiction in digital healthcare: A systematic review

B Chhetri, LM Goyal, M Mittal - International Journal of Information …, 2023 - Elsevier
Long-term use of drugs can sometimes result in brain damage that greatly affects a person's
psychology and sometimes become indecent. This paper examines psychological disorders …

Prevalence and correlates of cannabis use disorder among Australians using cannabis products to treat a medical condition

L Mills, N Lintzeris, M O'Malley, JC Arnold… - Drug and alcohol …, 2022 - Wiley Online Library
Introduction Prior research has examined the prevalence and correlates of cannabis use
disorder (CUD) in people who use cannabis; however, these are poorly described for …

Towards a New Dynamic Interaction Model of Adolescent CUD Manifestation, Prevention, and Treatment: A Narrative Review

W Oosten, E Vos, L Los, M Nelwan, T Pieters - Psychoactives, 2023 - mdpi.com
Background: Cannabis is one of the most popular drugs of the 21st century, especially
among adolescents and young adults. Evidence of a variety of lasting neuropsychological …

[HTML][HTML] Joint risk prediction for hazardous use of alcohol, cannabis, and tobacco among adolescents: A preliminary study using statistical and machine learning

TLM Ruberu, EA Kenyon, KA Hudson, F Filbey… - Preventive Medicine …, 2022 - Elsevier
For some, substance use during adolescence may be a stepping stone on the way to
substance use disorders in adulthood. Risk prediction models may help identify adolescent …

A Bayesian learning model to predict the risk for cannabis use disorder

RMDS Rajapaksha, F Filbey, S Biswas… - Drug and alcohol …, 2022 - Elsevier
Background The prevalence of cannabis use disorder (CUD) has been increasing recently
and is expected to increase further due to the rising trend of cannabis legalization. To help …

Utility of computational approaches for precision psychiatry: Applications to substance use disorders

J Vassileva, JH Lee, E Psederska, WY Ahn - Computational neuroscience, 2023 - Springer
Revolutionary advances in neuroscience and genetics over the past two decades have
provided unprecedented opportunities for increasing our understanding of the etiology and …

Classical and neural network machine learning to determine the risk of marijuana use

L Zoboroski, T Wagner, B Langhals - International Journal of …, 2021 - mdpi.com
Marijuana is the most commonly abused drug for military personnel tested at the Air Force
Drug Testing Laboratory. A publicly available dataset of drug use, personality trait scores …

Using decision trees to identify salient predictors of cannabis-related outcomes

FJ Schwebel, DK Richards, RA Pfund… - … of psychoactive drugs, 2022 - Taylor & Francis
Cannabis use continues to escalate among emerging adults and college attendance may be
a risk factor for use. Severe cases of cannabis use can escalate to a cannabis use disorder …

[HTML][HTML] The screening of cannabis addiction using machine learning, MoCA, and anxiety/depression tests

A Elhachimi, A Benksim, H Ibanni, M Cherkaoui - Scientific African, 2024 - Elsevier
A new cannabis addiction screening approach is developed based on the use of Machine
Learning (ML) alongside with psychological and cognitive assessment tests. The Hospital …

Risk prediction model for cannabis use with artificial intelligence approach

A Unlu, P Hakkarainen, K Karjalainen… - Journal of Substance …, 2024 - Taylor & Francis
Background Identifying the most important predictors of substance use is crucial for
developing effective prevention policies. Traditional statistical methods have some …