Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender.
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 i...
| Publicado en: | International Journal of Environmental Health Research Vol. 30; no. 4; pp. 435 - 447 |
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| Autores principales: | , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Aug2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144524419&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144524419 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09603123 57L jtl: International Journal of Environmental Health Research issn: 09603123 maglogo: Y pubinfo: dt: Aug2020 vid: 30 iid: 4 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 144524419 144524419 144524419 10.1080/09603123.2019.1597836 144524419 ppf: 435 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender. aug: au: Chen, Songjing Wu, Sizhu affil: Medical Information Innovation Research Center, Institute of Medical Information and Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China sug: subj: Deep Learning Respiratory Distress Syndrome, Acute Risk Factors Air Pollution Adverse Effects Environmental Exposure Data Mining Human China Male Female Neural Networks (Computer) Quantitative Studies Sulfur Compounds Nitrogen Compounds Particulate Matter Funding Source Male Female ab: 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 PM2.5 pollution than <50 years people, especially women ≥50 years. Compared with women, both men ≥50 years and <50 years were more susceptible to PM10. Young women <50 years were more sensitive to general air pollutants such as SO2 and NO2 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 (SO2, NO2, 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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