Harnessing machine learning in contemporary tobacco research
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.
Read the paper
Related studies
- Tobacco and COPD: presenting the World Health Organization (WHO) Tobacco Knowledge Summary
- Comparative effects of e-cigarette smoking on periodontal status, salivary pH, and cotinine levels
- Association between exposure to environmental pollutants and increased oral health risks, a comprehensive review
- Influence of gestational window on offspring vascular health in rodents with in utero exposure to electronic cigarettes
- Natural products as kinase inhibitors in lung cancer: molecular mechanisms, therapeutic potential, and clinical trials
- Nicotine in electronic cigarettes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.