Deep learning for Etiology of Chronic Kidney Disease in Taiwan

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

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
編輯Teen-Hang Meen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面322-325
頁數4
ISBN(電子)9781728180601
DOIs
出版狀態Published - 2020 十月 23
事件2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020 - Yunlin, Taiwan
持續時間: 2020 十月 232020 十月 25

出版系列

名字2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020

Conference

Conference2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020
國家Taiwan
城市Yunlin
期間20-10-2320-10-25

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

指紋 深入研究「Deep learning for Etiology of Chronic Kidney Disease in Taiwan」主題。共同形成了獨特的指紋。

引用此