Smoking & Tobacco

Spatiotemporal co-occurrence patterns and related risk factors of esophageal and lip and oral cavity cancers among older adults across 203 countries and territories: a machine learning-driven systematic analysis.

BACKGROUND: The country-level co-burden of esophageal cancer and lip and oral cavity cancer (LOC) among older adults remains poorly characterised. We examined global spatiotemporal patterns and associated population-level exposures. MATERIALS AND METHODS: We analyzed Global Burden of Disease 2021 data for adults ≥ 55 years across 203 countries and territories. Incidence rates were classified into ...

Bayesian Machine Learning for Estimating Optimal Dynamic Treatment Regimes With Ordinal Outcomes.

Dynamic treatment regimes (DTRs) are sequences of decision rules designed to tailor treatments based on patients' treatment history and evolving disease status. Ordinal outcomes frequently serve as primary endpoints in clinical trials and observational studies. However, constructing optimal DTRs for ordinal outcomes has been underexplored. This article introduces a Bayesian machine learning (BML) ...

Development of a Prognostic Survival Risk Score for Lung Cancer Patients With Type 2 Diabetes Mellitus: A Territory-Wide Retrospective Cohort Study.

BACKGROUND: Lung cancer is a leading cause of cancer-related mortality, and its prognosis is often affected by comorbidities such as type 2 diabetes m...

Identification of comorbidity-based clusters and mortality risk in hospitalised patients with diabetes mellitus in Karachi, Pakistan: a machine learning analysis.

BACKGROUND: Diabetes has reached epidemic proportions in Pakistan. This study applied machine learning (ML) techniques to identify comorbidity-based a...

Artificial intelligence and tobacco use: A bibliometric analysis 1997-2026.

INTRODUCTION: Globally, tobacco use is a key public health issue. Exploring the impact of developing AI technologies on tobacco use and intervention w...

Predictive Models of Pregnancy Based on Male Partner Data From a Preconception Cohort Study.

BACKGROUND: Male factors contribute to approximately 50% of couple infertility, yet few studies have used machine learning with comprehensive male par...

What does generative artificial intelligence mean for the future of nicotine and tobacco scientific publishing?

Artificial intelligence-enhanced management system for secondary prevention of coronary heart disease: a randomized clinical trial.

BACKGROUND: Secondary prevention of coronary heart disease (CHD) remains suboptimal due to fragmented care and therapeutic inertia. While digital heal...

Integrative Proteomics-Metabolomics Profiling of Umbilical Cord Plasma With Machine Learning Reveals Candidate Biomarkers for Gestational Diabetes Mellitus.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common pregnancy complication, yet its molecular mechanisms remain incompletely understood. This ...

Integrating Quantitative CT Biomarkers to Enhance COPD Detection in the HANSE Lung Cancer Screening Program.

BACKGROUND: Lung cancer screening offers an opportunity to enhance COPD detection among adults exposed to tobacco smoke, yet guidance on CT-based refe...

AI-generated smoking prevention videos on Instagram achieve greater reach when short-term consequences are addressed.

BACKGROUND: Tobacco use is a major preventable cause of cancer, and social media offers scalable opportunities for prevention. AI-generated virtual in...

Causal associations between hormone replacement therapy and brain structure: Evidence from large-scale Mendelian randomization and double machine learning.

BACKGROUND: Hormone replacement therapy (HRT) is widely prescribed for the management of hormone deficiency, particularly during menopause, yet its ca...

Real-time breath metabolomic profiling reveals a volatile signature associated with lung cancer.

BACKGROUND: Real-time breath metabolomic profiling may detect lung cancer-associated breath features, but exploratory case-control findings require ca...

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and valida...

Machine Learning Approaches to Identify Influential Factors of the Comorbid Psychiatric Symptoms and Hypertension Among Rural Adults in Bangladesh: A Cross-Sectional Study.

BACKGROUND AND AIM: The burden of both psychiatric symptoms (anxiety and/or depression) and hypertension poses significant public health challenges in...

What predicts postoperative opioid prescribing and consumption? A statewide analysis of surgical quality registry data.

OBJECTIVE: Tailoring postoperative opioid recommendations to patient needs requires nuanced understanding of factors contributing to post-discharge op...

Integrating network toxicology, machine learning, and single-cell sequencing systems to analyze autophagy core genes in lung adenocarcinoma.

The heterogeneity and complex tumor microenvironment of lung adenocarcinoma lead to poor prognosis. Autophagy, as a key cellular process, interacts wi...

The role of cellular senescence in sepsis and its mechanism in copper death: An analysis based on machine learning.

Sepsis is characterized by potentially fatal organ failure resulting from the host's abnormal response to infection. Due to the complex and rapid prog...

Deep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging.

PURPOSE: This project aims to develop and evaluate deep learning models using orbital magnetic resonance imaging for the prediction of continuous clin...

Classification of malignant/benign groups in lung cancer by machine learning and investigation of feature significance of parameters.

OBJECTIVES: This study aimed to apply machine learning (ML) models to enhance lung cancer (LC) classification, distinguishing malignant from benign tu...