P<sub>300</sub> auditory wave image and its relation to cognitive function in subjects with marijuana addiction: a cross-sectional study in Cipinang and Pondok Bambu Penitentiary, Jakarta

Zizlavsky, Semiramis; Alia, D.; Suwento, Ronny; Siste, Kristiana; Bardosono, Saptawati

doi:10.1088/1742-6596/1073/4/042039

Cited by 1 publication

(1 citation statement)

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“…Zehra and colleagues' study showed repeated marijuana use could cause neurobiological changes in the brain, resulting in addictive behavior and withdrawal symptoms [18]. A study confirmed that cognitive function impairment is caused by marijuana addiction [19]. In addition, marijuana use lowers birth weight and has destructive impacts on fetal growth in pregnant women [20].…”

Section: Introductionmentioning

confidence: 99%

Exploring Impact of Marijuana (Cannabis) Abuse on Adults Using Machine Learning

Choi

Chung

Choi

2021

IJERPH

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Marijuana is the most common illicit substance globally. The rate of marijuana use is increasing in young adults in the US. The current environment of legalizing marijuana use is further contributing to an increase of users. The purpose of this study was to explore the characteristics of adults who abuse marijuana (20–49 years old) and analyze behavior and social relation variables related to depression and suicide risk using machine-learning algorithms. A total of 698 participants were identified from the 2019 National Survey on Drug Use and Health survey as marijuana dependent in the previous year. Principal Component Analysis and Chi-square were used to select features (variables) and mean imputation method was applied for missing data. Logistic regression, Random Forest, and K-Nearest Neighbor machine-learning algorithms were used to build depression and suicide risk prediction models. The results showed unique characteristics of the group and well-performing prediction models with influential risk variables. Identified risk variables were aligned with previous studies and suggested the development of marijuana abuse prevention programs targeting 20–29 year olds with a regular depression and suicide screening. Further study is suggested for identifying specific barriers to receiving timely treatment for depression and suicide risk.

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Section: Introductionmentioning

confidence: 99%