AIMC Topic: Substance-Related Disorders
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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.
Utilizing RAG and GPT-4 for Extraction of Substance Use Information from Clinical Notes.
Studies in health technology and informatics
Nov 22, 2024
This research investigates the application of a hybrid Retrieval-Augmented Generation (RAG) and Generative Pre-trained Transformer (GPT) pipeline for extracting and categorizing substance use information from unstructured clinical notes. The aim is t...
From Notes to Knowledge: Deciphering Perioperative Risky Substance Use through Natural Language Processing.
Anesthesiology
Nov 1, 2024
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