AIMC Topic: Smoking
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A Robust Cross-Platform Solution With the Sense2Quit System to Enhance Smoking Gesture Recognition: Model Development and Validation Study.
Journal of medical Internet research
May 20, 2025
BACKGROUND: Smoking is a leading cause of preventable death, and people with HIV have higher smoking rates and are more likely to experience smoking-related health issues. The Sense2Quit study introduces innovative advancements in smoking cessation t...
A prediction model based on machine learning: prognosis of HBV-induced HCC male patients with smoking and drinking habits after local ablation treatment.
Frontiers in immunology
Mar 28, 2025
BACKGROUND: Liver cancer, particularly hepatocellular carcinoma (HCC), is a major health concern globally and in China, possibly shows recurrence after ablation treatment in high-risk patients. This study investigates the prognosis of early-stage mal...
Machine learning-enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta.
Proceedings of the National Academy of Sciences of the United States of America
Feb 10, 2025
The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential for environmental and health monitoring, for assessing toxicological exposure and their associated ...
Prediction of Aneurysm Sac Shrinkage After Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis.
The Journal of surgical research
Jan 9, 2025
INTRODUCTION: A simple risk stratification model to predict aneurysm sac shrinkagein patients undergoing endovascular aortic repair (EVAR) for abdominal aortic aneurysms (AAA) was developed using machine learning-based decision tree analysis.
Pulmonologists-level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach.
Scientific reports
Dec 24, 2024
Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated consi...
Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using Bioinformatics and Machine Learning Approaches.
International journal of environmental research and public health
Oct 22, 2024
Lung cancer (LC) is a significant global health issue, with smoking as the most common cause. Recent epidemiological studies have suggested that individuals who smoke are more susceptible to COVID-19. In this study, we aimed to investigate the influe...
Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.
NPJ systems biology and applications
Aug 2, 2024
Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, we developed interpretable predictive models for multi-omics data by employing neural networks inform...
Deep learning identifies histopathologic changes in bladder cancers associated with smoke exposure status.
PloS one
Jul 31, 2024
Smoke exposure is associated with bladder cancer (BC). However, little is known about whether the histologic changes of BC can predict the status of smoke exposure. Given this knowledge gap, the current study investigated the potential association be...
Machine learning computational model to predict lung cancer using electronic medical records.
Cancer epidemiology
Jul 24, 2024
BACKGROUND: Lung cancer (LC) screening using low-dose computed tomography (CT) is recommended according to standard risk criteria or personalized risk calculators. Machine learning (ML) models that can predict disease risk are an emerging method in m...
Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors.
BMC medical informatics and decision making
Jul 22, 2024
OBJECTIVE: To develop and validate machine learning models for predicting coronary artery disease (CAD) within a Taiwanese cohort, with an emphasis on identifying significant predictors and comparing the performance of various models.
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