Environmental Health

A Fast, Lightweight, and Generalizable Deep Neural Network for the Detection of Atrial Fibrillation

Atrial fibrillation (AFib) represents a critical diagnostic challenge in clinical cardiology, calling for automated detection systems capable of robust performance across diverse clinical environments. We present a computationally efficient deep neural network architecture for AFib detection that demonstrates exceptional generalizability despite training on a modest dataset. Our convolutional neur...

Automated Video-Based Analysis of Surgical Meta-competencies Using Computer Vision

Traditional surgical training relies on an apprenticeship model, which is subjective and threatened by human bias. Performance metric scales attempt to offer more objective feedback by providing a structured grading rubric, but these scores are still ultimately subjective. Leveraging computer vision and artificial intelligence to assess surgical performance has the potential to shift subjective me...

Modeling nonlinear and interaction effects of spatiotemporal and other non-genetic factors improves phenotypic prediction for complex traits

Adjusting for non-genetic factors can improve genetic association testing and polygenic prediction, yet most studies rely on linear adjustments for a ...

Comparing different types of machine learning models in diagnosing diabetes mellitus utilizing electrocardiography and clinical data

Diabetes Mellitus (DM) represents one of the most significant global public health challenges of the 21st century. This dramatic increase in the preva...

The Silent Signal: Unmasking Myocardial Ischemia in a Resting Heartbeat with Machine Learning

Ischemic heart disease (IHD) remains the leading cause of morbidity and mortality worldwide, imposing a staggering burden on healthcare systems and so...

Long-term, ambulatory 12-lead ECG from a single non-standard lead using perceptual reconstruction

Despite its broadening indications, the implantable cardiac monitor (ICM) records a narrow, nonstandard electrocardiogram (ECG) signal which precludes...

An Artificial Intelligence Model for Detection of Heart Failure with Preserved Ejection Fraction: A Report from HeartShare Study

Heart failure with preserved ejection fraction (HFpEF) accounts for over half of all heart failure cases in the United States and remains a diagnostic...

Demographics, Overlap, and Latency of Severe Cutaneous Adverse Reactions in an FDA Database

Severe cutaneous adverse reactions (SCARs), including Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS-TEN), drug reaction with eosinophilia a...

Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice.

OBJECTIVE: In non-clinical safety evaluation of drugs, pathological result is one of the gold standards for determining toxic effects. However, pathol...

Integration of smart sensors and phytoremediation for real-time pollution monitoring and ecological restoration in agricultural waste management.

Global climate change and ecological degradation highlight the urgency of dealing with agricultural waste and ecological restoration. Traditional poll...

Machine learning identifies genes linked to neurological disorders induced by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents.

Venezuelan, eastern, and western equine encephalitis viruses (collectively referred to as equine encephalitis viruses---EEV) cause serious neurologica...

A scoping review and quality assessment of machine learning techniques in identifying maternal risk factors during the peripartum phase for adverse child development.

Maternal exposure to environmental risk factors (e.g., heavy metal exposure) or mental health problems during the peripartum phase has been shown to l...

Machine Learning in Early Prediction of Metabolism of Drugs.

Machine learning (ML) has increasingly been applied to predict properties of drugs. Particularly, metabolism can be predicted with ML methods, which c...

Investigating the Impact of Antibiotics on Environmental Microbiota Through Machine Learning Models.

Antibiotic pollution in the environment can significantly impact soil microorganisms, such as altering the soil microbial community or emerging antibi...

Enhancing Marine Debris Acoustic Monitoring by Optical Flow-Based Motion Vector Analysis

With the development of coastal construction, a large amount of human-generated waste, particularly plastic debris, is continuously entering the oce...

[A novel approach for assessing quality of electrocardiogram signal by integrating multi-scale temporal features].

During long-term electrocardiogram (ECG) monitoring, various types of noise inevitably become mixed with the signal, potentially hindering doctors' ab...

Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation

Incorporating multi-modal features as side information has recently become a trend in recommender systems. To elucidate user-item preferences, recen...

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates

Urban pollution poses serious health risks, particularly in relation to traffic-related air pollution, which remains a major concern in many cities....

Early Concept Drift Detection via Prediction Uncertainty

Concept drift, characterized by unpredictable changes in data distribution over time, poses significant challenges to machine learning models in str...

Combining knowledge graphs and LLMs for hazardous chemical information management and reuse

Human health is increasingly threatened by exposure to hazardous substances, particularly persistent and toxic chemicals. The link between these sub...