Harnessing machine learning in contemporary tobacco research.

Q1 Environmental Science Toxicology Reports Pub Date : 2024-12-19 eCollection Date: 2025-06-01 DOI:10.1016/j.toxrep.2024.101877
Krishnendu Sinha, Nabanita Ghosh, Parames C Sil
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引用次数: 0

Abstract

Machine learning (ML) has the potential to transform tobacco research and address the urgent public health crisis posed by tobacco use. Despite the well-documented health risks, cessation rates remain low. ML techniques offer innovative solutions by analyzing vast datasets to uncover patterns in smoking behavior, genetic predispositions, and effective cessation strategies. ML can predict smoking-induced non-communicable diseases (SiNCDs) like lung cancer and postmenopausal osteoporosis by identifying biomarkers and genetic profiles, generating personalized predictions, and guiding interventions. It also improves prediction of infant tobacco smoke exposure, distinguishes secondhand and thirdhand smoke, and enhances protection strategies for children. Data-driven, personalized approaches using ML track real-time data for personalized feedback and offer timely interventions, continuously improving cessation strategies. Overall, ML provides sophisticated predictive models, enhances understanding of complex biological mechanisms, and enables personalized interventions, demonstrating significant potential in the fight against the tobacco epidemic.

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来源期刊
Toxicology Reports
Toxicology Reports Environmental Science-Health, Toxicology and Mutagenesis
CiteScore
7.60
自引率
0.00%
发文量
228
审稿时长
11 weeks
期刊最新文献
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