AIMC Topic: Substance-Related Disorders
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Predictors of treatment attrition among individuals in substance use disorder treatment: A machine learning approach.
Addictive behaviors
Jan 25, 2025
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
Jan 7, 2025
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
Dec 9, 2024
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
Dec 9, 2024
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)
Dec 2, 2024
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
Aug 14, 2024
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
Jul 31, 2024
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...
Evaluating generative AI responses to real-world drug-related questions.
Psychiatry research
Jun 26, 2024
Generative Artificial Intelligence (AI) systems such as OpenAI's ChatGPT, capable of an unprecedented ability to generate human-like text and converse in real time, hold potential for large-scale deployment in clinical settings such as substance use ...
Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.
Journal of substance use and addiction treatment
Jun 8, 2024
BACKGROUND: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-leve...
IUPHAR Review: New strategies for medications to treat substance use disorders.
Pharmacological research
Jan 20, 2024
Substance use disorders (SUDs) and drug overdose are a public health emergency and safe and effective treatments are urgently needed. Developing new medications to treat them is expensive, time-consuming, and the probability of a compound progressing...
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