Smoking & Tobacco

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 function, leading to death or irreversible organ damage if not addressed immediately. Emergency department (ED)-based IHCA (EDCA) accounts for 10% to 20% of all IHCA cases. Early detection of EDCA is crucial, yet identifying subtle signs of cardiac det...

Intracranial stenosis prediction using a small set of risk factors in the Tromsø Study.

Intracranial atherosclerotic stenosis (ICAS) refers to a narrowing of intracranial arteries due to plaque buildup on the inside of the vessel walls restricting blood flow. Early detection of ICAS is crucial to prevent serious consequences such as stroke. Here we apply three different machine learning methods, such as support vector machines, multi-layer perceptrons and Kolmogorov-Arnold Networks t...

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...

Machine Learning Analysis of Nutrient Associations with Peripheral Arterial Disease: Insights from NHANES 1999-2004.

BACKGROUND: Peripheral arterial disease (PAD) is a common manifestation of atherosclerosis, affecting over 200 million people worldwide. The incidence...

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...

Predicting doxorubicin-induced cardiotoxicity in breast cancer: leveraging machine learning with synthetic data.

Doxorubicin (DOXO) is a primary treatment for breast cancer but can cause cardiotoxicity in over 25% of patients within the first year post-chemothera...

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...

Analyzing Secondary Cancer Risk: A Machine Learning Approach.

OBJECTIVE: Addressing the rising cancer rates through timely diagnosis and treatment is crucial. Additionally, cancer survivors need to understand the...

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...