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Substance-Related Disorders

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Factors Associated with HIV Viral Suppression Among Transgender Women in Lima, Peru.

LGBT health
PURPOSE: Globally, transgender women (TGW) experience a high burden of adverse health outcomes, including a high prevalence of HIV and sexually transmitted infections (STIs) as well as psychiatric disorders and substance use disorders. To address gap...

Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion.

Biological psychiatry. Cognitive neuroscience and neuroimaging
BACKGROUND: Successfully treating illicit drug use has become paramount, yet elusive. Devising specialized treatment interventions could increase positive outcomes, but it is necessary to identify risk factors of poor long-term outcomes to develop sp...

Is Treatment Readiness Associated With Substance Use Treatment Engagement? An Exploratory Study.

Journal of drug education
With nearly 8.2% of Americans experiencing substance use disorders (SUDs), a need exists for effective SUD treatment and for strategies to assist treatment participants to complete treatment programs (Chandler, Fletcher, & Volkow, 2009). The purpose ...

Using Neural Multi-task Learning to Extract Substance Abuse Information from Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Substance abuse carries many negative health consequences. Detailed information about patients' substance abuse history is usually captured in free-text clinical notes. Automatic extraction of substance abuse information is vital to assess patients' ...

Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum.

Journal of medical Internet research
BACKGROUND: Online discussion forums allow those in addiction recovery to seek help through text-based messages, including when facing triggers to drink or use drugs. Trained staff (or "moderators") may participate within these forums to offer guidan...

Identifying substance use risk based on deep neural networks and Instagram social media data.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Social media may provide new insight into our understanding of substance use and addiction. In this study, we developed a deep-learning method to automatically classify individuals' risk for alcohol, tobacco, and drug use based on the content from th...

Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.

Journal of affective disorders
BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Predominant polarity (PP) appears to be an important specifier of BD. The present study employed machine lea...

Applications of machine learning in addiction studies: A systematic review.

Psychiatry research
This study aims to provide a systematic review of the applications of machine learning methods in addiction research. In this study, multiple searches on MEDLINE, Embase and the Cochrane Database of Systematic Reviews were performed. 23 full-text art...

A Machine Learning Approach for the Detection and Characterization of Illicit Drug Dealers on Instagram: Model Evaluation Study.

Journal of medical Internet research
BACKGROUND: Social media use is now ubiquitous, but the growth in social media communications has also made it a convenient digital platform for drug dealers selling controlled substances, opioids, and other illicit drugs. Previous studies and news i...