AIMC Topic: Environmental Exposure

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Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender.

International journal of environmental health research
This study focuses on identifying environmental health risk factors related to acute respiratory diseases using deep learning method. Based on respiratory disease data, air pollution data and meteorological environmental data, cross-domain risk facto...

Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach.

BMC medical research methodology
BACKGROUND: Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative...

Association between prenatal exposure to multiple persistent organic pollutants (POPs) and growth indicators in newborns.

Environmental research
Despite the fact that many of persistent organic pollutants (POPs) have been banned for decades, they still constitute a group of harmful substances to human health. Prenatal exposure can have adverse effects on one's health as well as on their newbo...

Multiple Human-Behaviour Indicators for Predicting Lung Cancer Mortality with Support Vector Machine.

Scientific reports
Lung cancer is still one of the most common causes of death around the world, while there is overwhelming evidence that the environment and lifestyle factors are predominant causes of most sporadic cancers. However, when applying human-behaviour indi...

Urinary concentrations and profiles of organophosphate and pyrethroid pesticide metabolites and phenoxyacid herbicides in populations in eight countries.

Environment international
Concentrations of nine metabolites of organophosphate and pyrethroid insecticides, as well as two phenoxy herbicides, were determined in 322 urine samples collected from eight countries during 2006-2014 by high-performance liquid chromatography-tande...

Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks.

Environmental pollution (Barking, Essex : 1987)
Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum ...

Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence.

Journal of exposure science & environmental epidemiology
Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemica...

Prediction of bioconcentration factors in fish and invertebrates using machine learning.

The Science of the total environment
The application of machine learning has recently gained interest from ecotoxicological fields for its ability to model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However, comparison of different mode...

Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology.

Environmental health : a global access science source
Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to ex...