AI Medical Compendium Journal:
Environment international

Showing 31 to 40 of 55 articles

New approach methodologies in human regulatory toxicology - Not if, but how and when!

Environment international
The predominantly animal-centric approach of chemical safety assessment has increasingly come under pressure. Society is questioning overall performance, sustainability, continued relevance for human health risk assessment and ethics of this system, ...

Predicting chemical ecotoxicity by learning latent space chemical representations.

Environment international
In silico prediction of chemical ecotoxicity (HC) represents an important complement to improve in vivo and in vitro toxicological assessment of manufactured chemicals. Recent application of machine learning models to predict chemical HC yields varia...

Predicting chemical hazard across taxa through machine learning.

Environment international
We applied machine learning methods to predict chemical hazards focusing on fish acute toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup, showing that taking them into account can lead to considerable improvements in ...

Evaluation of a semi-automated data extraction tool for public health literature-based reviews: Dextr.

Environment international
INTRODUCTION: There has been limited development and uptake of machine-learning methods to automate data extraction for literature-based assessments. Although advanced extraction approaches have been applied to some clinical research reviews, existin...

Walking-induced exposure of biological particles simulated by a children robot with different shoes on public floors.

Environment international
Inhalation exposure to the resuspended biological particles from public places can cause adverse effects on human health. In this work, carpet dust samples were first collected from twenty example conference and hotel rooms by a vacuum cleaner. A bip...

Understanding global changes in fine-mode aerosols during 2008-2017 using statistical methods and deep learning approach.

Environment international
Despite their extremely small size, fine-mode aerosols have significant impacts on the environment, climate, and human health. However, current understandings of global changes in fine-mode aerosols are limited. In this study, we employed newly devel...

Ensemble-based deep learning for estimating PM over California with multisource big data including wildfire smoke.

Environment international
INTRODUCTION: Estimating PM concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in nat...

New interpretable deep learning model to monitor real-time PM concentrations from satellite data.

Environment international
Particulate matter with a mass concentration of particles with a diameter less than 2.5 μm (PM) is a key air quality parameter. A real-time knowledge of PM is highly valuable for lowering the risk of detrimental impacts on human health. To achieve th...

SWIFT-Active Screener: Accelerated document screening through active learning and integrated recall estimation.

Environment international
BACKGROUND: In the screening phase of systematic review, researchers use detailed inclusion/exclusion criteria to decide whether each article in a set of candidate articles is relevant to the research question under consideration. A typical review ma...

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

Environment international
Various additives are used in plastic products to improve the properties and the durability of the plastics. Their possible elution from the plastics when plastics are fragmented into micro- and nano-size in the environment is suspected to one of the...