AIMC Topic: Substance-Related Disorders
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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' ...
Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion.
Biological psychiatry. Cognitive neuroscience and neuroimaging
Aug 1, 2017
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
Apr 10, 2017
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
Jan 28, 2016
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
Nov 5, 2015
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...
Ethical aspects and user preferences in applying machine learning to adjust eHealth addressing substance use: A mixed-methods study.
International journal of medical informatics
Jul 1, 2025
BACKGROUND: Digital health interventions targeting substance use disorders are being increasingly implemented. Data science methodology has the potential to enhance involvement and efficacy of these interventions, though application may raise ethical...
Building and Beta-Testing Be Well Buddy Chatbot, a Secure, Credible and Trustworthy AI Chatbot That Will Not Misinform, Hallucinate or Stigmatize Substance Use Disorder: Development and Usability Study.
JMIR human factors
May 7, 2025
BACKGROUND: Artificially intelligent (AI) chatbots that deploy natural language processing and machine learning are becoming more common in health care to facilitate patient education and outreach; however, generative chatbots such as ChatGPT face ch...
Leveraging artificial intelligence-mediated communication for cancer prevention and control and drug addiction: A systematic review.
Translational behavioral medicine
Jan 16, 2025
OBJECTIVE: To conduct a systematic review on Artificial Intelligence-Mediated Communication (AIMC) behavioral interventions in cancer prevention/control and substance use.
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