Machine Learning-Based Nicotine Addiction Prediction Models for Youth E-Cigarette and Waterpipe (Hookah) Users

Choi, June-Seek; Jung, Hee-Tae; Ferrell, Anastasiya; Woo, Seoyoon; Haddad, Linda

doi:10.3390/jcm10050972

Cited by 14 publications

(10 citation statements)

References 61 publications

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“…It is defined only by the number of trees and the depth of each tree. For a study using the random forest technique for addiction problems, please refer to [ 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Problematic Smartphone Use: A Machine Learning Approach

Lee

Kim

2021

IJERPH

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While smartphone addiction is becoming a recent concern with the exponential increase in the number of smartphone users, it is difficult to predict problematic smartphone users based on the usage characteristics of individual smartphone users. This study aimed to explore the possibility of predicting smartphone addiction level with mobile phone log data. By Korea Internet and Security Agency (KISA), 29,712 respondents completed the Smartphone Addiction Scale developed in 2017. Integrating basic personal characteristics and smartphone usage information, the data were analyzed using machine learning techniques (decision tree, random forest, and Xgboost) in addition to hypothesis tests. In total, 27 variables were employed to predict smartphone addiction and the accuracy rate was the highest for the random forest (82.59%) model and the lowest for the decision tree model (74.56%). The results showed that users’ general information, such as age group, job classification, and sex did not contribute much to predicting their smartphone addiction level. The study can provide directions for future work on the detection of smartphone addiction with log-data, which suggests that more detailed smartphone’s log-data will enable more accurate results.

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“…It is defined only by the number of trees and the depth of each tree. For a study using the random forest technique for addiction problems, please refer to [ 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Problematic Smartphone Use: A Machine Learning Approach

Lee

Kim

2021

IJERPH

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“…Machine-learning has found steadily increasing applications in the addiction literature. Supervised ML methods have been used to predict adolescent alcohol use (102) and misuse (103), distinguish between smokers and non-smokers (104)(105)(106), between people with and without cocaine use disorder (107,108) or cannabis use disorder (109)(110)(111), and between people with different types of SUD (107,(112)(113)(114)(115)(116). These ML studies have identified multivariate neurobiological, neurocognitive, psychiatric, and personality profiles that differentiate addictions to different classes of drugs.…”

Section: Data-driven Approaches / Machine Learningmentioning

confidence: 99%

Utility of computational approaches for precision psychiatry: Applications to substance use disorders

Vassileva¹,

Lee²,

Psederska³

et al. 2022

Preprint

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Revolutionary advances in neuroscience and genetics over the past two decades have provided unprecedented opportunities for increasing our understanding of the etiology and pathogenesis of psychiatric disorders. Despite these advances, the translation of this knowledge into clinical practice has been hindered by the significant heterogeneity within disorders and the neurobiologically-imprecise categorization of patients. Traditional diagnostic categories do not capture the underlying neurobiological processes and etiological mechanisms of psychiatric disorders and cannot adequately inform prognosis and treatment. To address this gap, the National Institute on Mental Health (NIMH) has proposed an alternative research framework based on Research Domain Criteria (RDoC), which yields psychiatric classification grounded on discoveries from neuroscience and genomics. Recently, the RDoC approach has been adapted for the study of addictions with the Addictions Neuroclinical Assessment (ANA) framework (1), which proposes that the assessment of addictions should cover multiple systems and focus on three key neurofunctional domains: Executive Function, Incentive Salience, and Negative Emotionality. Building upon the aims of the RDoC framework, a new field of “precision psychiatry” has emerged, considered to be a paradigm shift in psychiatry. Both the RDoC framework and precision psychiatry are predicated on precise measurement, which has necessitated the development of novel analytic approaches for classification and novel dimensional tools for phenotyping that can identify the unique mechanisms of psychiatric disorders at an individual level. In this chapter, we make the case that theory-driven and data-driven computational approaches have enormous potential for increasing the precision and reliability of measurement, and the accuracy of diagnosis and prognosis in psychiatry. We review three types of computational approaches and their utility for precision psychiatry: (1) Theory-driven approaches, such as computational modeling; (2) Data-driven approaches, using various machine learning methods; and (3) Hybrid approaches, such as joint modeling and adaptive design optimization. We focus more narrowly on the application of these approaches to substance use disorders (SUD), where we attempt to map them on the current RDoC framework for addictions.

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“…Therefore, our ML analysis can identify the important predictors that may be focused on for targeted polices and prevention programs. ML has been used in ENDS research ( Choi et al, 2021 , Fu et al, 2021 , Han et al, 2021 ), including in young adults ( Atuegwu et al, 2020 ). The goal in this study was to use ML to create predictive models and identify multiple prospective predictors of ENDS initiation in tobacco-naive young adults.…”

Section: Introductionmentioning

confidence: 99%