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

Autor:	Bickel WK; Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA., Tomlinson DC; Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA.; Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, VA, USA., Craft WH; Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA.; Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, VA, USA., Ma M; Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA., Dwyer CL; Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA.; Department of Psychology, Virginia Tech, Blacksburg, VA, USA., Yeh YH; Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA., Tegge AN; Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA.; Department of Statistics, Virginia Tech, Blacksburg, VA, USA., Freitas-Lemos R; Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA., Athamneh LN; Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA.
Jazyk:	angličtina
Zdroj:	Addiction neuroscience [Addict Neurosci] 2023 Jun; Vol. 6. Date of Electronic Publication: 2023 Jan 31.
DOI:	10.1016/j.addicn.2023.100068
Abstrakt:	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. Competing Interests: Although the following activities/relationships do not create a conflict of interest pertaining to this manuscript, in the interest of full disclosure, Dr. Bickel would like to report the following: W. K. Bickel is a principal of HealthSim, LLC; BEAM Diagnostics, Inc.; and Red 5 Group, LLC. In addition, he serves on the scientific advisory board for Sober Grid, Inc., and Ria Health, is a consultant for Alkermes, Inc., and works on a project supported by Indivior, Inc. The other authors have no conflicts to report.
Databáze:	MEDLINE
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