Artificial intelligence in medicine · 2025
Types, functions, and mechanisms of machine learning for personalizing smoking cessation interventions: A systematic scoping review.
СтатьяScoping ReviewПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Ng Yu Jie Xavia, Cheong Shing Hui Reina, Ang Wen Wei, Lau Ying, Lau Siew Tiang
Аннотация
PURPOSE: Artificial intelligence can realize personalization. This systematic scoping review provides the types, functions, and mechanisms of machine learning (ML) for personalizing smoking cessation interventions.
METHODOLOGY: We searched fourteen databases including PubMed, CINAHL, EMBASE, the Cochrane Library, IEEE Xplore, PsycINFO, Scopus, Web of Science, AAAI, ACM Digital Library, ArXIV, Mednar, ProQuest, and Science.gov. We selected 98 articles from 4073 records that met the criteria. Two independent reviewers screened and selected the articles. Two reviewers extracted the data using a self-developed data charting form independently.
RESULTS: The findings are reported in narrative syntheses, tables, and figures. The types of ML included artificial neural networks, Bayesian algorithms, clustering algorithms, decision tree algorithms, deep learning (DL) algorithms, ensemble algorithms, linear classifiers, others, and unspecified. The most common ML technique used was supervised learning (81 %), and the ML functions included (1) message tailoring (17 %), (2) prediction and detection of smoking events (34 %), (3) social media surveillance (14 %), (4) predictive models (24 %), and (5) biomarker analysis (10 %). The ML mechanisms involved the following sequence: data input, data preprocessing, feature extraction and selection, training and validation, and data output.
CONCLUSION: This review is the first to describe the potential use of ML for personalizing smoking cessation interventions. We provide recommendations for future research by identifying the limitations and gaps in the studies. Future studies should refine, validate, and test ML models using robust experimental methods to conclude their effectiveness.
METHODOLOGY: We searched fourteen databases including PubMed, CINAHL, EMBASE, the Cochrane Library, IEEE Xplore, PsycINFO, Scopus, Web of Science, AAAI, ACM Digital Library, ArXIV, Mednar, ProQuest, and Science.gov. We selected 98 articles from 4073 records that met the criteria. Two independent reviewers screened and selected the articles. Two reviewers extracted the data using a self-developed data charting form independently.
RESULTS: The findings are reported in narrative syntheses, tables, and figures. The types of ML included artificial neural networks, Bayesian algorithms, clustering algorithms, decision tree algorithms, deep learning (DL) algorithms, ensemble algorithms, linear classifiers, others, and unspecified. The most common ML technique used was supervised learning (81 %), and the ML functions included (1) message tailoring (17 %), (2) prediction and detection of smoking events (34 %), (3) social media surveillance (14 %), (4) predictive models (24 %), and (5) biomarker analysis (10 %). The ML mechanisms involved the following sequence: data input, data preprocessing, feature extraction and selection, training and validation, and data output.
CONCLUSION: This review is the first to describe the potential use of ML for personalizing smoking cessation interventions. We provide recommendations for future research by identifying the limitations and gaps in the studies. Future studies should refine, validate, and test ML models using robust experimental methods to conclude their effectiveness.