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 This study used machine learning (ML) to test an empirically derived set of risk
factors for marijuana use. Models were built separately for child welfare (CW) and non-CW …

Background Substance use, including the non-medical use of prescription medications, is a
global health problem resulting in hundreds of thousands of overdose deaths and other …

FIGURE 2. Progression of electronic large language model (LLM) technology. 1.
Transformer models are probably the basic infrastructure for these technologies (1). 2 …

Objective The purpose of this research is to identify how data science is applied in suicide
prevention literature, describe the current landscape of this literature and highlight areas …

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 …

Abstract Background The United States (US) continues to grapple with a drug overdose
crisis. While opioids remain the main driver of overdose deaths, deaths involving …

Although several recent studies have examined psychosocial and demographic correlates
of cannabis use disorder (CUD) in adults, few, if any, recent studies have evaluated the …

Introduction There is ongoing concern about the impact of increasing use of social media
and digital devices on unhealthy behaviors such as substance use in youth. Mobile phone …

Background Substance use induces large economic and societal costs in the US
Understanding the change in substance use behaviors of persons who use drugs (PWUDs) …