Applications of machine learning in addiction studies: A systematic review
KK Mak, K Lee, C Park - Psychiatry research, 2019 - Elsevier
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 …
methods in addiction research. In this study, multiple searches on MEDLINE, Embase and …
Computational theory-driven studies of reinforcement learning and decision-making in addiction: What have we learned?
MCM Gueguen, EM Schweitzer, AB Konova - Current opinion in behavioral …, 2021 - Elsevier
Highlights•Computational psychiatry holds promise for mechanistic discovery in
addiction.•This approach captures latent factors driving behavioral differences from …
addiction.•This approach captures latent factors driving behavioral differences from …
Machine learning applications in tobacco research: a scoping review
Objective Identify and review the body of tobacco research literature that self-identified as
using machine learning (ML) in the analysis. Data sources MEDLINE, EMABSE, PubMed …
using machine learning (ML) in the analysis. Data sources MEDLINE, EMABSE, PubMed …
Computational mechanisms of addiction: recent evidence and its relevance to addiction medicine
Abstract Purpose of Review In this article, we provide a brief review of recent computational
modelling studies of substance use disorders (SUDs), with a focus on work published within …
modelling studies of substance use disorders (SUDs), with a focus on work published within …
Dysfunctional feedback processing in male methamphetamine abusers: Evidence from neurophysiological and computational approaches
Methamphetamine use disorder (MUD) as a major public health risk is associated with
dysfunctional neural feedback processing. Although dysfunctional feedback processing in …
dysfunctional neural feedback processing. Although dysfunctional feedback processing in …
Translational models of addiction phenotypes to advance addiction pharmacotherapy
Alcohol and substance use disorders are heterogeneous conditions with limited effective
treatment options. While there have been prior attempts to classify addiction subtypes, they …
treatment options. While there have been prior attempts to classify addiction subtypes, they …
Computational approaches and machine learning for individual-level treatment predictions
MP Paulus, WK Thompson - Psychopharmacology, 2021 - Springer
Rationale The impact of neuroscience-based approaches for psychiatry on pragmatic
clinical decision-making has been limited. Although neuroscience has provided insights into …
clinical decision-making has been limited. Although neuroscience has provided insights into …
Practical foundations of machine learning for addiction research. Part I. Methods and techniques
P Cresta Morgado, M Carusso… - The American Journal …, 2022 - Taylor & Francis
Machine learning assembles a broad set of methods and techniques to solve a wide range
of problems, such as identifying individuals with substance use disorders (SUD), finding …
of problems, such as identifying individuals with substance use disorders (SUD), finding …
Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task
Despite progress in smoking reduction in the past several decades, cigarette smoking
remains a significant public health concern world-wide, with many smokers attempting but …
remains a significant public health concern world-wide, with many smokers attempting but …
Anhedonia in nicotine dependence
DG Gilbert, BM Stone - Anhedonia: Preclinical, Translational, and Clinical …, 2022 - Springer
Prior findings indicate that trait anhedonia enhances the likelihood of becoming a tobacco
smoker, and preliminary evidence suggests that smoking abstinence leads to anhedonic …
smoker, and preliminary evidence suggests that smoking abstinence leads to anhedonic …