Smoking & Tobacco

Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations.

This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk factors via machine learning techniques. Data from 14,145 workers (627 with occupational noise-induced hearing loss (ONIHL)) at Hebei Medical Examination Center (2017-2023) were analyzed. Workers with combined exposure and without specific contraindic...

Lung cancer detection with machine learning classifiers with multi-attribute decision-making system and deep learning model.

Diseases of the airways and the other parts of the lung cause chronic respiratory diseases. The major cause of lung disease is tobacco smoke, along with risk factors such as dust, air pollution, chemicals, and frequent lower respiratory infections during childhood. Early detection of these diseases requires the analysis of medical images, which would aid doctors in providing effective treatment.Th...

AI integrations with lung cancer screening: Considerations in developing AI in a public health setting.

Lung cancer screening implementation has led to expanded imaging of the chest in older, tobacco-exposed populations. Growing numbers of screening case...

Deep Learning-Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study.

BACKGROUND: In-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory fu...

Machine learning-enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta.

The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential...

A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus.

Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or reviewing recorded behaviour, which are laborious,...

Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms.

Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate intervention due to neuropsychological issues. Howeve...

Residual networks using multi-task learning algorithm for near-infrared spectroscopy: A case study.

Near-infrared spectroscopy (NIRS) is a widely used non-destructive detection method known for its efficiency and environmental friendliness. However, ...

Retrieval of nicotine content in cigar leaves by remote analysis of aerial hyperspectral combining machine learning methods.

Cigar leaf is a special type of tobacco plant, which is the raw material for producing high-quality cigars. The content and proportion of nicotine and...

A deep learning model for QRS delineation in organized rhythms during in-hospital cardiac arrest.

BACKGROUND: Cardiac arrest (CA) is the sudden cessation of heart function, typically resulting in loss of consciousness and cessation of pulse and bre...

Immunological composition of human milk before and during subclinical and clinical mastitis.

Mastitis, an inflammatory condition affecting more than 25% of breastfeeding women, is usually associated with reduced milk secretion, pain, and disco...

A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.

Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of dust particles in the lungs, is characterized by c...

Predicting early cessation of exclusive breastfeeding using machine learning techniques.

BACKGROUND: Identification of mother-infant pairs predisposed to early cessation of exclusive breastfeeding is important for delivering targeted suppo...

Key risk factors of generalized anxiety disorder in adolescents: machine learning study.

Adolescents worldwide are increasingly affected by mental health disorders, with anxiety disorders, including Generalized Anxiety Disorder (GAD), bein...

Quality prediction of air-cured cigar tobacco leaf using region-based neural networks combined with visible and near-infrared hyperspectral imaging.

Visible and Near-infrared hyperspectral imaging (VNIR-HSI) combined with machine learning has shown its effectiveness in various detection application...

Pulmonologists-level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach.

Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection ...

Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density.

Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environm...

Low-Cost Approaches in Neuroscience to Teach Machine Learning Using a Cockroach Model.

In an effort to increase access to neuroscience education in underserved communities, we created an educational program that utilizes a simple task to...

Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning.

BACKGROUND: Several studies indicate that smoking is one of the major risk factors for bladder cancer. Nicotine and its metabolites, the main componen...

Integrating Metabolomics Domain Knowledge with Explainable Machine Learning in Atherosclerotic Cardiovascular Disease Classification.

Metabolomic data often present challenges due to high dimensionality, collinearity, and variability in metabolite concentrations. Machine learning (ML...