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

Bickel, Warren K.; Tomlinson, Devin C; Craft, William H; Ma, Mingxing; Dwyer, Candice L.; Yeh, Yu‐Hua; Tegge, Allison N.; Freitas‐Lemos, Roberta

doi:10.1016/j.addicn.2023.100068

Cited by 6 publications

(2 citation statements)

References 55 publications

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“…Emerging directions like computational methods and traditional Chinese medicine approaches may complement continued progress in established domains like cessation pharmacotherapy and public policy (Bickel et al 2023). Artificial intelligence and machine learning show great promise for advancing tobacco and nicotine research (Bendotti et al 2023)…”

Section: Future Research Directionsmentioning

confidence: 99%

Oxidative stress and inflammation: elucidating mechanisms of smoking-attributable pathology for therapeutic targeting

Addissouky,

El Sayed,

Ali

et al. 2024

Bull Natl Res Cent

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Background Tobacco smoking remains a major preventable cause of disease and death worldwide. Combustible cigarettes release thousands of chemicals that can initiate inflammatory pathways leading to smoking-related illness. This review aims to synthesize current scientific knowledge on mechanisms of smoking-induced disease, epidemiological trends, and clinical strategies from recent literature. Main body of the abstract At the cellular level, cigarette smoke triggers oxidative stress through reactive oxygen species (ROS), causing DNA damage. This provokes inflammatory signaling cascades mediated by damage-associated molecular patterns (DAMPs), receptors like RAGE and TLRs, and downstream cytokines. Smoking also disrupts apoptosis and autophagy. In the lungs, oxidative stress and inflammation from smoking play central roles in COPD pathogenesis. Smoking-induced oxidative DNA damage, chronic inflammation, and impaired immunity combine to promote lung carcinogenesis. For cardiovascular disease, smoking triggers endothelial dysfunction, platelet activation, and atherogenesis through oxidized LDL and effects on nitric oxide and adhesion molecules. Short conclusion Given the unequivocal evidence of health risks, smoking cessation is critical to reducing preventable death and disability. Both counseling and pharmacotherapy have proven efficacy for quitting, but efficacy remains limited long-term. Emerging nicotine products like e-cigarettes have unknown impacts on cessation and population health. Comprehensive efforts encompassing prevention, screening, treatment innovation, harm reduction, and policy reform focused on curbing smoking-attributable morbidity and mortality are warranted.

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Section: Future Research Directionsmentioning

confidence: 99%

Oxidative stress and inflammation: elucidating mechanisms of smoking-attributable pathology for therapeutic targeting

Addissouky,

El Sayed,

Ali

et al. 2024

Bull Natl Res Cent

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“…Over the past years, studies on ML and the combination of different ML methods have become a trend (Ren et al, 2022), and a growing body of research is applying ML in several areas, such as business -considering ML a tool that can be used in cryptocurrency research (Ren et al, 2022), for stock market prediction (Mintarya et al, 2023); in health and medical research, there are studies for prediction in medical and surgical research (Srinivas & Young, 2023), to identify the predictors of smoking (Bickel et al, 2023); ML based methodologies have also accelerated the prediction of the physical properties of materials (Magar & Farimani, 2023), and many others. However, ML is still experimental because no universal learning algorithm exists, even though the number of ML algorithms is extensive and growing (Liu et al, 2021;Nguyen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing

De Oliveira Santos,

Schneider Hahn

2023

Multidiscip. bus. rev (Impr.)

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The purpose of this paper is to analyse the use of machine learning in smart manufacturing, describing techniques, technologies, industries, and purposes associated with industrial applications. We conducted a systematic literature review using Scopus, in which 26,032 documents were found. After applying quality criteria, 107 articles were analysed. The main findings show that machinery was the industry subsector with the major implementations regarding machine learning; process improvement is the main concern (interest) of all implementations; random forest was the most specific machine learning technique used; and diverse technologies associated with this context were identified such as: the industrial internet of things, digital twin, sensor technologies (soft, optical, barometric, ultrasonic), software technologies (Python, MATLAB, LabView, Google AutoML Platform) and equipment technologies (robotic, PLC, CNC). Most fault detection machine learning applications were focused on predictive maintenance, specifically in mechanical equipment (bearings, machines in general, and assembly lines). This study presents a novel taxonomy that identifies 85 specific machine-learning techniques used in smart manufacturing.

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Editorial - Advancing Biomarkers for Treatment of Smoking and Nicotine Dependence: An Overview

Pollock

Wanke

Compton

2023

Addiction Neuroscience

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

Cited by 6 publications

References 55 publications

Oxidative stress and inflammation: elucidating mechanisms of smoking-attributable pathology for therapeutic targeting

Oxidative stress and inflammation: elucidating mechanisms of smoking-attributable pathology for therapeutic targeting

A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing

Editorial - Advancing Biomarkers for Treatment of Smoking and Nicotine Dependence: An Overview

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