Public Health & Policy

Environmental Health

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

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Artificial intelligence based detection and control strategies for river water pollution: A comprehensive review.

Water quality (WQ) is a metric for assessing the overall health and safety of water bodies like a ri...

The need for epistemic humility in AI-assisted pain assessment.

It has been difficult historically for physicians, patients, and philosophers alike to quantify pain...

Automated detection of arrhythmias using a novel interpretable feature set extracted from 12-lead electrocardiogram.

The availability of large-scale electrocardiogram (ECG) databases and advancements in machine learni...

AI-aided chronic mixture risk assessment along a small European river reveals multiple sites at risk and pharmaceuticals being the main risk drivers.

The vast amount of registered chemicals leads to a high diversity of substances occurring in the env...

Prediction of acute toxicity of organic contaminants to fish: Model development and a novel approach to identify reactive substructures.

In this study, count-based Morgan fingerprints (CMF) were employed to represent the fundamental chem...

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 majo...

PM concentration prediction using machine learning algorithms: an approach to virtual monitoring stations.

One of the most important pollutants is PM, which is particularly important to monitor pollutant lev...

Comprehensive Raman spectroscopy analysis for differentiating toxic cyanobacteria through multichannel 1D-CNNs and SHAP-based explainability.

Cyanobacterial blooms pose significant environmental and public health risks due to the production o...

Uncovering key sources of regional ozone simulation biases using machine learning and SHAP analysis.

Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still ha...

Explainable deep learning models for predicting water pipe failures.

Failures within water distribution networks (WDNs) lead to significant environmental and economic im...

Prediction of surface water pollution using wavelet transform and 1D-CNN.

Permanganate index (COD), total nitrogen, and ammonia nitrogen are important indicators that represe...

Prediction of river dissolved oxygen (DO) based on multi-source data and various machine learning coupling models.

Too low a concentration of dissolved oxygen (DO) in a river can disrupt the ecological balance, whil...

Reliability and validity of a novel single-lead portable electrocardiogram device for pregnant women: a comparative study.

BACKGROUND: WenXinWuYang, a novel portable Artificial Intelligence Electrocardiogram (AI-ECG) device...

Measuring the Level of Aflatoxin Infection in Pistachio Nuts by Applying Machine Learning Techniques to Hyperspectral Images.

This paper investigates the use of machine learning techniques on hyperspectral images of pistachios...

PM concentration prediction using a whale optimization algorithm based hybrid deep learning model in Beijing, China.

PM is a significant global atmospheric pollutant impacting visibility, climate, and public health. A...

Air pollution and prostate cancer: Unraveling the connection through network toxicology and machine learning.

BACKGROUND: In recent years, air pollution has been demonstrated to be associated with the occurrenc...

Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms.

A large number of mycotoxins and related fungal metabolites have not been assessed in terms of their...

Predicting Toxicity toward Nitrifiers by Attention-Enhanced Graph Neural Networks and Transfer Learning from Baseline Toxicity.

Assessing chemical environmental impacts is critical but challenging due to the time-consuming natur...

Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City.

Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid in...

Prediction of school PM by an attention-based deep learning approach informed with data from nearby air quality monitoring stations.

Predicting indoor air pollutants concentrations in schools is essential for ensuring a healthy learn...

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