Elan Barenholtz1, Nicole D Fitzgerald1,2, William Edward Hahn3. 1. Department of Psychology, Center for Complex Systems and Brain Sciences, Florida Atlantic University. 2. Department of Epidemiology, University of Florida. 3. Department of Mathematical Sciences, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, Florida, USA.
Abstract
PURPOSE OF REVIEW: To provide an accessible overview of some of the most recent trends in the application of machine learning to the field of substance use disorders and their implications for future research and practice. RECENT FINDINGS: Machine-learning (ML) techniques have recently been applied to substance use disorder (SUD) data for multiple predictive applications including detecting current abuse, assessing future risk and predicting treatment success. These models cover a wide range of machine-learning techniques and data types including physiological measures, longitudinal surveys, treatment outcomes, national surveys, medical records and social media. SUMMARY: The application of machine-learning models to substance use disorder data shows significant promise, with some use cases and data types showing high predictive accuracy, particularly for models of physiological and behavioral measures for predicting current substance use, portending potential clinical diagnostic applications; however, these results are uneven, with some models performing poorly or at chance, a limitation likely reflecting insufficient data and/or weak validation methods. The field will likely benefit from larger and more multimodal datasets, greater standardization of data recording and rigorous testing protocols as well as greater use of modern deep neural network models applied to multimodal unstructured datasets.
PURPOSE OF REVIEW: To provide an accessible overview of some of the most recent trends in the application of machine learning to the field of substance use disorders and their implications for future research and practice. RECENT FINDINGS: Machine-learning (ML) techniques have recently been applied to substance use disorder (SUD) data for multiple predictive applications including detecting current abuse, assessing future risk and predicting treatment success. These models cover a wide range of machine-learning techniques and data types including physiological measures, longitudinal surveys, treatment outcomes, national surveys, medical records and social media. SUMMARY: The application of machine-learning models to substance use disorder data shows significant promise, with some use cases and data types showing high predictive accuracy, particularly for models of physiological and behavioral measures for predicting current substance use, portending potential clinical diagnostic applications; however, these results are uneven, with some models performing poorly or at chance, a limitation likely reflecting insufficient data and/or weak validation methods. The field will likely benefit from larger and more multimodal datasets, greater standardization of data recording and rigorous testing protocols as well as greater use of modern deep neural network models applied to multimodal unstructured datasets.
Authors: Jonatan Ottino-González; Anne Uhlmann; Sage Hahn; Zhipeng Cao; Renata B Cupertino; Nathan Schwab; Nicholas Allgaier; Nelly Alia-Klein; Hamed Ekhtiari; Jean-Paul Fouche; Rita Z Goldstein; Chiang-Shan R Li; Christine Lochner; Edythe D London; Maartje Luijten; Sadegh Masjoodi; Reza Momenan; Mohammad Ali Oghabian; Annerine Roos; Dan J Stein; Elliot A Stein; Dick J Veltman; Antonio Verdejo-García; Sheng Zhang; Min Zhao; Na Zhong; Neda Jahanshad; Paul M Thompson; Patricia Conrod; Scott Mackey; Hugh Garavan Journal: Drug Alcohol Depend Date: 2021-11-25 Impact factor: 4.492
Authors: Carl B Roth; Andreas Papassotiropoulos; Annette B Brühl; Undine E Lang; Christian G Huber Journal: Int J Environ Res Public Health Date: 2021-08-05 Impact factor: 3.390