Environmental Health

Deep Learning to Unveil Correlations between Urban Landscape and Population Health.

The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in pollution exposure and a more sedentary lifestyle. Healthcare providers deal with increasing new challenges, and thanks to fast-developing big data te...

The Effect of Resampling on Data-imbalanced Conditions for Prediction towards Nuclear Receptor Profiling Using Deep Learning.

In toxicity evaluation based on the nuclear receptor signalling pathway, in silico prediction tools are used for the detection of the early stages of long-term toxicities, the prioritization of newly synthesized chemicals and the acquisition of the selectivity and sensitivity. Computational prediction model is one of the promising tools for the toxicity screening of the chemical-protein interactio...

A Stochastic Multi-Attribute Method for Measuring Sustainability Performance of a Supplier Based on a Triple Bottom Line Approach in a Dual Hesitant Fuzzy Linguistic Environment.

China is a developing country and with the speeding up of its industrialization, the environmental problems are becoming more serious, environmental p...

Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.

Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long...

Non-Standardized Patch-Based ECG Lead Together With Deep Learning Based Algorithm for Automatic Screening of Atrial Fibrillation.

This study was to assess the feasibility of using non-standardized single-lead electrocardiogram (ECG) monitoring to automatically detect atrial fibri...

Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data.

Nowcasting of precipitation is a difficult spatiotemporal task because of the non-uniform characterization of meteorological structures over time. Rec...

A water quality prediction method based on the multi-time scale bidirectional long short-term memory network.

As an important factor affecting the mangrove wetland ecosystem, water quality has become the focus of attention in recent years. Therefore, many stud...

Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms.

BACKGROUND: Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify different types of cardiac arrhythmias with the use of a singl...

Highly Sensitive Detection of Chemically Modified Thio-Organophosphates by an Enzymatic Biosensing Device: An Automated Robotic Approach.

Pesticides represent some of the most common man-made chemicals in the world. Despite their unquestionable utility in the agricultural field and in th...

The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors.

Metabolism of drugs affects their absorption, distribution, efficacy, excretion, and toxicity profiles. Metabolism is routinely assessed experimentall...

Modeling and optimization of imidacloprid degradation by catalytic percarbonate oxidation using artificial neural network and Box-Behnken experimental design.

Due to its toxicity and persistence, pesticide pollution poses a serious threat to human health and the environment. Imidacloprid or IMD is an archety...

Combination of compositional data analysis and machine learning approaches to identify sources and geochemical associations of potentially toxic elements in soil and assess the associated human health risk in a mining city.

Mining activities change the chemical composition of the environment and have negative reflection on people's health and there is no single measure to...

Comprehensive evaluation of benefits from environmental investment: take China as an example.

With the rapid development of social economy and the increasing environmental pollution in China, the government's environmental investment has been g...

Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCast™ and deep learning models combined approach.

Various additives are used in plastic products to improve the properties and the durability of the plastics. Their possible elution from the plastics ...

New criteria and new methodological tools for devising criteria sets of inflammatory rheumatic diseases.

Rheumatologists use classification criteria to separate patients with inflammatory rheumatic diseases (IRD). They change over time, and the concepts o...

Hybrid decision tree-based machine learning models for short-term water quality prediction.

Water resources are the foundation of people's life and economic development, and are closely related to health and the environment. Accurate predicti...

Machine learning and ligand binding predictions: A review of data, methods, and obstacles.

Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of m...

Image Based Liver Toxicity Prediction.

The drugs we use to cure our diseases can cause damage to the liver as it is the primary organ responsible for metabolism of environmental chemicals a...

Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality.

(1) Medical research has shown an increasing interest in machine learning, permitting massive multivariate data analysis. Thus, we developed drug into...

Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease.

Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and ...