Predictors of smoking cessation outcomes identified by machine learning: A systematic review
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Addiction Neuroscience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772392523000081 |
| _version_ | 1797847716534943744 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Warren K. Bickel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T18:15:53Z |
| format | Article |
| id | doaj.art-9b58aec1e6e345008c091dbd0af03f41 |
| institution | Directory Open Access Journal |
| issn | 2772-3925 |
| language | English |
| last_indexed | 2024-04-09T18:15:53Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Addiction Neuroscience |
| spelling | doaj.art-9b58aec1e6e345008c091dbd0af03f412023-04-13T04:27:29ZengElsevierAddiction Neuroscience2772-39252023-06-016100068Predictors of smoking cessation outcomes identified by machine learning: A systematic reviewWarren K. Bickel0Devin C. Tomlinson1William H. Craft2Manxiu Ma3Candice L. Dwyer4Yu-Hua Yeh5Allison N. Tegge6Roberta Freitas-Lemos7Liqa N. Athamneh8Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA; Corresponding author.Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA; Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, VA, USAFralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA; Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, VA, USAFralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USAFralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA; Department of Psychology, Virginia Tech, Blacksburg, VA, USAFralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USAFralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA; Department of Statistics, Virginia Tech, Blacksburg, VA, USAFralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USAFralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USAThis 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.http://www.sciencedirect.com/science/article/pii/S2772392523000081Smoking cessationMachine learningSystematic review |
| spellingShingle | 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 Predictors of smoking cessation outcomes identified by machine learning: A systematic review Addiction Neuroscience Smoking cessation Machine learning Systematic review |
| title | Predictors of smoking cessation outcomes identified by machine learning: A systematic review |
| title_full | Predictors of smoking cessation outcomes identified by machine learning: A systematic review |
| title_fullStr | Predictors of smoking cessation outcomes identified by machine learning: A systematic review |
| title_full_unstemmed | Predictors of smoking cessation outcomes identified by machine learning: A systematic review |
| title_short | Predictors of smoking cessation outcomes identified by machine learning: A systematic review |
| title_sort | predictors of smoking cessation outcomes identified by machine learning a systematic review |
| topic | Smoking cessation Machine learning Systematic review |
| url | http://www.sciencedirect.com/science/article/pii/S2772392523000081 |
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