Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender.
Journal:
International journal of environmental health research
Published Date:
Mar 31, 2019
Abstract
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 factors of acute respiratory diseases were identified in Beijing, China. We conducted age and gender stratified deep neural network models in air pollution epidemiology. We ranked risk factors of respiratory diseases in stratified populations and conducted quantitative comparison. People ≥50 years were more sensitive to PM pollution than <50 years people, especially women ≥50 years. Compared with women, both men ≥50 years and <50 years were more susceptible to PM. Young women <50 years were more sensitive to general air pollutants such as SO and NO than <50 years young men. Meteorological factors such as wind speed and precipitation could promote the diffusion of fine particulate matter and general air pollutants (SO, NO, etc.), which could help to reduce the incidence of acute respiratory diseases. This study represents a quantitative analysis of environmental health risk factors identification related to acute respiratory diseases based on deep neural network method. The results of this study could help people to improve their awareness of acute respiratory diseases prevention.
Authors
Keywords
Acute Disease
Adolescent
Adult
Age Factors
Aged
Aged, 80 and over
Air Pollutants
Beijing
Child
Child, Preschool
China
Deep Learning
Environmental Exposure
Female
Humans
Incidence
Infant
Infant, Newborn
Male
Middle Aged
Particulate Matter
Respiratory Tract Diseases
Risk Factors
Sex Factors
Weather
Young Adult