Predictors of smoking cessation outcomes identified by machine learning: A systematic review

Addiction neuroscience Pub Date : 2023-06-01 DOI:10.1016/j.addicn.2023.100068

Warren K. Bickel , Devin C. Tomlinson , William H. Craft , Manxiu Ma , Candice L. Dwyer , Yu-Hua Yeh , Allison N. Tegge , Roberta Freitas-Lemos , Liqa N. Athamneh

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引用次数: 2

Abstract

This systematic review aims to characterize the utility of machine learning to identify the predictors of smoking cessation outcomes and identify the machine learning methods applied in this area. In the current study, multiple searches occurred through December 9, 2022 in MEDLINE, Science Citation Index, Social Science Citation Index, EMBASE, CINAHL Plus, APA PsycINFO, PubMed, Cochrane Central Register of Controlled Trials, and the IEEE Xplore were performed. Inclusion criteria included various machine learning techniques, studies reporting cigarette smoking cessation outcomes (smoking status and the number of cigarettes), and various experimental designs (e.g., cross-sectional and longitudinal). Predictors of smoking cessation outcomes were assessed, including behavioral markers, biomarkers, and other predictors. Our systematic review identified 12 papers fitting our inclusion criteria. In this review, we identified gaps in knowledge and innovation opportunities for machine learning research in the field of smoking cessation.

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机器学习确定的戒烟结果预测因素:系统综述

这篇系统综述旨在描述机器学习在确定戒烟结果预测因素方面的效用，并确定在该领域应用的机器学习方法。在目前的研究中，截至2022年12月9日，在MEDLINE、科学引文索引、社会科学引文指数、EMBASE、CINAHL Plus、APA PsycINFO、PubMed、Cochrane对照试验中央注册中心和IEEE Xplore中进行了多次搜索。纳入标准包括各种机器学习技术、报告戒烟结果（吸烟状态和香烟数量）的研究以及各种实验设计（例如，横截面和纵向）。评估了戒烟结果的预测因素，包括行为标志物、生物标志物和其他预测因素。我们的系统综述确定了12篇符合我们纳入标准的论文。在这篇综述中，我们发现了戒烟领域机器学习研究在知识和创新机会方面的差距。

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来源期刊

Addiction neuroscience Neuroscience (General)

CiteScore

1.30

自引率

0.00%

发文量

审稿时长

118 days