AIMC Topic: Substance-Related Disorders

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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' ...

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...

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...

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...

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 ...

Biosignature Discovery for Substance Use Disorders Using Statistical Learning.

Trends in molecular medicine
There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' ...

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...

Use of a machine learning framework to predict substance use disorder treatment success.

PloS one
There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitat...

A Comparison of Natural Language Processing Methods for Automated Coding of Motivational Interviewing.

Journal of substance abuse treatment
Motivational interviewing (MI) is an efficacious treatment for substance use disorders and other problem behaviors. Studies on MI fidelity and mechanisms of change typically use human raters to code therapy sessions, which requires considerable time,...

Automated Extraction of Substance Use Information from Clinical Texts.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Within clinical discourse, social history (SH) includes important information about substance use (alcohol, drug, and nicotine use) as key risk factors for disease, disability, and mortality. In this study, we developed and evaluated a natural langua...