Smoking & Tobacco

Interpretable machine-learning survival prediction of breast cancer prognosis from lifestyle factors: evidence from UK biobank.

BACKGROUND: Understanding how lifestyle habits influence breast cancer survival is crucial for improving long-term outcomes and guiding individualized care. The purpose of this study is to comprehensively understand how dietary habits, exercise frequency, sleep quality and the frequency of tobacco and alcohol use affect the survival of breast cancer patients by analyzing the synergistic effects of...

An Acceptance Criteria Framework for Determining the Implementation Fit of Custom Large Language Models in Public Health Interventions.

Large language models (LLMs) are increasingly embedded in clinical and population health workflows, including conversational agents such as health chatbots. As chatbots evolve from rule-based approaches to hybrid and LLM-enabled designs, risks and concerns about deployment readiness shift. Unlike rule-based chatbots, LLM outputs can be unpredictable, error-prone, and difficult to validate with tra...

An AI-driven quality assessment, pesticide residue investigation and chemical compound analysis system for clustering pepper products.

This study investigated the colour values (L*, a*, b*, C* and H°), pesticide residue (PR) levels, and chemical compound compositions of red pepper fla...

Raman spectroscopic detection of salivary thiocyanate for smoking exposure stratification.

Distinguishing smokers from non-smokers and stratifying smokers based on their smoking history/exposure (years) are essential for evaluating the risk ...

Integrative multi-omics identifies CYP1B1 as a candidate molecular link between toxicant exposure and ferroptosis-related epithelial stress in COPD.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is characterized by progressive airflow limitation, chronic airway inflammation, and epitheli...

Frailty burden and symptomatic BPH/LUTS in aging men: evidence from two nationally representative population-based studies.

BACKGROUND: The frailty index (FI) is a well-established marker of biological aging and a widely validated predictor of adverse health outcomes in old...

Prediction of future onset of allergic rhinitis and analysis of risk factors in children with food allergy during infancy.

UNLABELLED: The atopic march describes the progression from infantile food allergy (FA) to allergic rhinitis (AR) in later childhood. However, not all...

Non-advanced age-related macular degeneration: current concepts and future perspectives.

Non-advanced age-related macular degeneration (AMD), encompassing early and intermediate stages, represents a critical therapeutic window before irrev...

Machine learning-based prediction of 3-6-month post-stroke cognitive impairment using acute-phase clinical data: a two-center retrospective prognostic modeling study.

BACKGROUND: Post-stroke cognitive impairment (PSCI) is common and disabling, but identifying patients at risk early remains difficult. We developed an...

Automatic detection of e-cigarette screens using object detection and vision language models.

INTRODUCTION: The e-cigarette market evolves faster than regulation and enforcement. In recent years, e-cigarette manufacturers have introduced vapes ...

Where You Live Shapes Diabetes Risk: Multilevel Determinants of Type 2 Diabetes in the East South Central United States.

Type 2 diabetes melittus (T2DM) remains a major public health challenge in the United States, particularly in the east south central regions. his stud...

Social Determinants of Health and Fentanyl Overdose Mortality Across US Counties: An XGBoost and SHAP Analysis Identifying Silent Risk Counties and Treatment Deserts.

BACKGROUND: Fentanyl overdose deaths are still increasing across the U.S. Even though the crisis is growing, we still do not fully understand which co...

Global Geospatial Trends in Breast Cancer and Atrial Fibrillation/Flutter Among Older Women: Uncovering Shared Epidemiological Patterns and Etiological Links.

BACKGROUND: Breast cancer (BC) and atrial fibrillation (AF) represent increasing global health burdens with shared risk factors. However, their coinci...

Machine learning-based identification of medication adherence predictors in two Spanish primary care cohorts.

Non-adherence to medication represents an important global challenge that compromises patient outcomes and increases healthcare costs, particularly in...

AI-based CT quantification reveals small airway loss and vascular simplification in rheumatoid arthritis-associated lung disease.

OBJECTIVES: To investigate pulmonary structural changes in patients with rheumatoid arthritis (RA) using an artificial intelligence (AI)-based CT segm...

Predictors of vaping relapse among young e-cigarette users: Machine learning-based survival analysis of a smartphone app-derived longitudinal data.

INTRODUCTION: Although many young people attempt to quit vaping nicotine, limited evidence exists on factors associated with relapse. We aimed to pred...

Life's essential 8 and its role in cardiovascular health: evolution, evidence, and future directions.

INTRODUCTION: Cardiovascular disease (CVD) remains the leading global cause of mortality. To advance prevention, the American Heart Association introd...

Admission-Based Machine-Learning Models for Predicting Mechanical Ventilation and Mortality in Fibrotic Interstitial Lung Disease: A Multicenter Cohort Study with External Validation.

BACKGROUND: Admission-based risk stratification tools are limited for hospitalized patients with fibrotic interstitial lung disease (F-ILD). AIMS: To ...

Opportunistic screening for incident cardiometabolic disease in metabolically healthy non-obese individuals: a prospective cohort study.

BACKGROUND: Metabolically healthy non-obese (MHN) individuals are considered at low cardiometabolic risk, yet a subset may harbor unfavorable visceral...

Using Natural Language Processing of Clinical Notes to Supplement Structured Electronic Health Record Data for Phenotyping Smoking and Obesity in a Healthcare System.

PURPOSE: Studies based on electronic health records (EHR) often rely on structured data, which may incompletely capture important clinical phenotypes ...