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Machine Learning Algorithms to Predict Heavy Episodic Drinking in the United States Using Survey Data

Bibliographic Data

ID21597039
AuthorsLaura Llamosas-Falcón (0000-0003-4718-9376, Institute for Mental Health Policy Research Centre for Addiction and Mental Health Toronto Canada, corresponding author), Charlotte Probst (0000-0003-4360-697X, Institute for Mental Health Policy Research Centre for Addiction and Mental Health Toronto Canada), Kevin D Shield (0000-0003-1871-8849, Centre for Addiction and Mental Health), Kevin Shield (Institute for Mental Health Policy Research Centre for Addiction and Mental Health Toronto Canada), Erik Spence (SciNet Consortium University of Toronto Toronto Canada), Jurgen Rehm (0000-0001-5665-0385, Institute for Mental Health Policy Research Centre for Addiction and Mental Health Toronto Canada)
Year2026
Volume45
Issue1
Pagese70065-e70065
Publication date2026-01-01
Open AccessYes
TypeARTICLE
VenueDrug and Alcohol Review (JOURNAL)
Journal identifiersISSN: 0959-5236 • E-ISSN: 1465-3362
PublisherWiley (PUBLISHER • GB)
DOI10.1111/dar.70065
PMID41189345
OpenAlexW4415905951
LanguageEN
References cited34

Discriminative model · Model validation · Predictive modelling · Survey data collection · Test data · Advanced Causal Inference Techniques · Imbalanced Data Classification Techniques · Substance Abuse Treatment and Outcomes

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  • Binge drinking

    Emmanuel Kuntsche, Sandra Kuntsche et al.•Psychology & Health•2017

  • Building Predictive Models in R Using the caret Package

    Open Access•Max Kühn•Journal of Statistical Software•2008

  • Table 2 Fallacy in Descriptive Epidemiology

    Open Access•Christoffer Dharma, Rui Fu et al.•International Journal of…•2023

Highly citedNo

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