Public Health & Policy

Environmental Health

Latest AI and machine learning research in environmental health for healthcare professionals.

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Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation.

OBJECTIVE: The prevalence of atrial fibrillation (AF) in the general population is 0.5%-1%. As AF is...

Robot-guided pediatric stereoelectroencephalography: single-institution experience.

OBJECTIVEStereoelectroencephalography (SEEG) has increased in popularity for localization of epilept...

Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

The majority of computational methods for predicting toxicity of chemicals are typically based on "n...

Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings.

The development of new technology such as wearables that record high-quality single channel ECG, pro...

Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings.

Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly p...

Identification of lead anti-human cytomegalovirus compounds targeting MAP4K4 via machine learning analysis of kinase inhibitor screening data.

Chemogenomic approaches involving highly annotated compound sets and cell based high throughput scre...

Effect of Cd and Pb Pollutions on Physiological Growth: Wavelet Neural Network (WNN) as a New Approach on Age Determination of Coenobita scaevola.

Environmental pollution of aquatic ecosystems leads to an interference in several fundamental bioche...

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination.

This study aimed to develop a novel framework for risk assessment of nitrate groundwater contaminati...

A support vector machine approach for AF classification from a short single-lead ECG recording.

OBJECTIVE: In this paper, a support vector machine (SVM) approach using statistical features, P wave...

Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram.

OBJECTIVE: In this paper, we propose a convolutional neural network (CNN)-based deep learning archit...

A novel application of deep learning for single-lead ECG classification.

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac ...

Forecasting air quality time series using deep learning.

UNLABELLED: This paper presents one of the first applications of deep learning (DL) techniques to pr...

Tracking antibiotic resistance gene pollution from different sources using machine-learning classification.

BACKGROUND: Antimicrobial resistance (AMR) has been a worldwide public health concern. Current wides...

Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM in the Northeastern USA.

Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pol...

Mechanism & inhibition kinetics of bioassay-guided fractions of Indian medicinal plants and foods as ACE inhibitors.

Hypertension is a becoming a major threat to the world. Angiotensin converting enzyme (ACE) is a key...

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from...

Application of Bayesian networks in a hierarchical structure for environmental risk assessment: a case study of the Gabric Dam, Iran.

Environmental risk assessment (ERA) is a commonly used, effective tool applied to reduce adverse eff...

Integrating river hydromorphology and water quality into ecological status modelling by artificial neural networks.

The aim of the study was to develop predictive models of the ecological status of rivers by using ar...

Evaluation of a novel automated water analyzer for continuous monitoring of toxicity and chemical parameters in municipal water supply.

A novel tool, the DAMTA analyzer (Device for Analytical Monitoring and Toxicity Assessment), designe...

A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length.

OBJECTIVE: Atrial fibrillation (AF) is a major cause of hospitalization and death in the United Stat...

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