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Substance-Related Disorders

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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...

Comparison of hepatitis B and SARS-CoV2 vaccination rates in people who attended Drugs and Addiction Centres.

Frontiers in public health
BACKGROUND AND AIMS: Persons with substance use disorder are at increased risk for hepatitis B virus (HBV) infection. Although most of them are attached to social health centers, the vaccination rate in this group is low. In this context, we designed...

IUPHAR Review: New strategies for medications to treat substance use disorders.

Pharmacological research
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...

Evaluating generative AI responses to real-world drug-related questions.

Psychiatry research
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
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...

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...

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...

Utilizing RAG and GPT-4 for Extraction of Substance Use Information from Clinical Notes.

Studies in health technology and informatics
This research investigates the application of a hybrid Retrieval-Augmented Generation (RAG) and Generative Pre-trained Transformer (GPT) pipeline for extracting and categorizing substance use information from unstructured clinical notes. The aim is t...

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.