AIMC Topic: Substance-Related Disorders
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Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder.
Drug and alcohol dependence
Oct 22, 2019
BACKGROUND: Substance use disorder (SUD) exacts enormous societal costs in the United States, and it is important to detect high-risk youths for prevention. Machine learning (ML) is the method to find patterns and make prediction from data. We hypoth...
Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity.
Drug and alcohol dependence
Oct 1, 2019
BACKGROUND: This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics.
A Machine Learning Approach for the Detection and Characterization of Illicit Drug Dealers on Instagram: Model Evaluation Study.
Journal of medical Internet research
Jun 15, 2019
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...
Applications of machine learning in addiction studies: A systematic review.
Psychiatry research
Mar 4, 2019
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...
Using Neural Multi-task Learning to Extract Substance Abuse Information from Clinical Notes.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
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
Nov 6, 2018
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...
Demography
Feeding and Eating Disorders
Hallucinations
Delusions
Substance-Related Disorders
Area Under Curve
Alcoholism
Tobacco Use Disorder
Cohort Studies
Female
ROC Curve
Hospitalization
Age Factors
Adult
Middle Aged
Bipolar Disorder
Cross-Sectional Studies
Male
Humans
Machine Learning
Algorithms
Identifying substance use risk based on deep neural networks and Instagram social media data.
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Oct 24, 2018
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
Jun 12, 2018
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
Feb 27, 2018
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
Feb 4, 2018
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' ...
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