The application of machine-learning models to substance use disorder data shows
significant promise, with some use cases and data types showing high predictive accuracy …

Objective Identify and review the body of tobacco research literature that self-identified as
using machine learning (ML) in the analysis. Data sources MEDLINE, EMABSE, PubMed …

Identifying determinants of smoking cessation is critical for develo** optimal cessation
treatments and interventions. Machine learning (ML) is becoming more prevalent for …

Forensic evaluations conducted with children and adolescents are replete with a host of
complex and nuanced challenges. Adding to this complexity are the recent societal trends …

There is a lack of rigorous methodological development for descriptive epidemiology, where
the goal is to describe and identify the most important associations with an outcome given a …

Prior studies have found an especially high prevalence of illicit substance use among
adolescents and young adults in Brazil. The current study aimed to employ machine …

Objective: Youth may develop posttraumatic stress disorder (PTSD) following a hurricane.
Triaging of mental health services is crucial to effectively deliver trauma-focused …

Substance use poses a significant public health challenge worldwide, including in Finland.
This study seeks to predict patterns of substance use, aiming to identify the driving factors …

Substance Use Disorder (SUD) affects about 5% of adolescents and can lead to many
personal and societal problems. Risk factors such as peer pressure, permissive parenting …

Substances of abuse have the potential to cause addiction, habituation or altered
consciousness. Most of the research on these substances focuses on addiction, and is …