AIMC Topic: Substance-Related Disorders

Clear Filters Showing 11 to 20 of 67 articles

Improving diagnosis-based quality measures: an application of machine learning to the prediction of substance use disorder among outpatients.

BMJ open quality
OBJECTIVE: Substance use disorder (SUD) is clinically under-detected and under-documented. We built and validated machine learning (ML) models to estimate SUD prevalence from electronic health record (EHR) data and to assess variation in facility-lev...

Machine Learning-Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: To address gaps in global understanding of cultural and social variations, this study used a high-performance machine learning (ML) model to predict adolescent substance use across three national datasets.

Machine learning to detect recent recreational drug use in intensive cardiac care units.

Archives of cardiovascular diseases
BACKGROUND: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this iss...

Predictors of treatment attrition among individuals in substance use disorder treatment: A machine learning approach.

Addictive behaviors
BACKGROUND: Early treatment discontinuation in substance use disorder treatment settings is common and often difficult to predict. We leveraged a machine learning approach (i.e., random forest) to identify individuals at risk for treatment attrition,...

Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning.

Scientific reports
With the growing global challenge of drug abuse, there is an urgent need for rapid, accurate, and cost-effective drug detection methods. This study introduces an innovative approach to drug abuse screening by quickly detecting ephedrine (EPH) in tear...

Predicting drug craving among ketamine-dependent users through machine learning based on brain structural measures.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Craving is a core factor driving drug-seeking and -taking, representing a significant risk factor for relapse. This study aims to identify neuroanatomical biomarkers for quantifying and predicting craving.

Flexibility and resilience in equity-centered research: lessons learned conducting a randomized controlled trial of a family-based substance use prevention program for American Indian families.

Frontiers in public health
Meaningful and effective community engagement lies at the core of equity-centered research, which is a powerful tool for addressing health disparities in American Indian (AI) communities. It is essential for centering Indigenous wisdom as a source of...

Artificial Intelligence-driven and technological innovations in the diagnosis and management of substance use disorders.

International review of psychiatry (Abingdon, England)
Substance Use Disorders (SUD) lead to a collection of health challenges such as overdoses and clinical diseases. Populations that are vulnerable and lack straightforward treatment access are vulnerable to significant economic and social effects linke...

The metabolic clock of ketamine abuse in rats by a machine learning model.

Scientific reports
Ketamine has recently become an anesthetic drug used in human and veterinary clinical medicine for illicit abuse worldwide, but the detection of illicit abuse and inference of time intervals following ketamine abuse are challenging issues in forensic...

Improving treatment completion for young adults with substance use disorder: Machine learning-based prediction algorithms.

Journal of psychiatric research
Substance use disorder (SUD) treatment completion was intertwined with various factors. However, few studies have explored the intersections of psychosocial and system-related factors with SUD treatment completion, particularly for individuals receiv...