AIMC Topic: Substance-Related Disorders

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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...

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...

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...

A relational agent for treating substance use in adults: Protocol for a randomized controlled trial with a psychoeducational comparator.

Contemporary clinical trials
BACKGROUND: Substance use disorders (SUDs) are prevalent and compromise health and wellbeing. Scalable solutions, such as digital therapeutics, may offer a population-based strategy for addressing SUDs. Two formative studies supported the feasibility...

Predicting Substance Use Treatment Failure with Transfer Learning.

Substance use & misuse
Transfer learning, which involves repurposing a trained model on a related task, may allow for better predictions with substance use data than models that are trained using the target data alone. This approach may also be useful for small clinical da...

Long-term results after robot-assisted radical prostatectomy of a simplified inguinal hernia prevention technique without artificial substance use.

International journal of urology : official journal of the Japanese Urological Association
INTRODUCTION: Durable techniques that prevent postoperative inguinal hernia (IH) after robot-assisted radical prostatectomy (RARP) have not been established. This study evaluated the long-term efficacy of a postoperative IH prevention technique that ...

A Bayesian mixed effects support vector machine for learning and predicting daily substance use disorder patterns.

The American journal of drug and alcohol abuse
Substance use disorder (SUD) is a heterogeneous disorder. Adapting machine learning algorithms to allow for the parsing of intrapersonal and interpersonal heterogeneity in meaningful ways may accelerate the discovery and implementation of clinically...

Predicting changes in substance use following psychedelic experiences: natural language processing of psychedelic session narratives.

The American journal of drug and alcohol abuse
: Experiences with psychedelic drugs, such as psilocybin or lysergic acid diethylamide (LSD), are sometimes followed by changes in patterns of tobacco, opioid, and alcohol consumption. But, the specific characteristics of psychedelic experiences that...