Deep learning for Etiology of Chronic Kidney Disease in Taiwan

Sheng Min Chiu, Feng Jung Yang, Yi Chung Chen, Chiang Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The link between air pollution and chronic kidney disease has been broadly examined by researchers. However, the relationship between the two remains unclear. Establishing this link has been complicated in part by the fact that air quality varies considerably from place to place. Therefore, this study designed a deep learning model that analyzed the relationship between air pollution data and chronic kidney disease. The experiments utilized real hospital data in Taiwan. Furthermore, we verified that the methods could help hospital teams in Taiwan better understand the association of air pollution and chronic kidney disease and also proposed subsequent and effective medical improvement plans.

Original languageEnglish
Title of host publication2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
EditorsTeen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages322-325
Number of pages4
ISBN (Electronic)9781728180601
DOIs
Publication statusPublished - 2020 Oct 23
Event2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020 - Yunlin, Taiwan
Duration: 2020 Oct 232020 Oct 25

Publication series

Name2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020

Conference

Conference2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020
CountryTaiwan
CityYunlin
Period20-10-2320-10-25

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation

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