Predicting post-stroke pneumonia using deep neural network approaches.

Journal: International journal of medical informatics
Published Date: Oct 1, 2019

Abstract

BACKGROUND AND PURPOSE: Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely and accurate prediction of post-stroke pneumonia would be highly valuable in clinical practice. Previous pneumonia risk score models were often built on simple statistical methods such as logistic regression. This study aims to investigate post-stroke pneumonia prediction models using more advanced machine learning algorithms, specifically deep learning approaches.

Authors

  • Yanqiu Ge
    School of Chemistry, Dalian University of Technology, Dalian 116023, PR China.
  • Qinghua Wang
  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Honghu Wu
    Department of Information, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
  • Chen Peng
    Department of Pharmacy, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
  • Jiajing Wang
    School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Medical School, Nanchang University, Nanchang, China.
  • Yuan Xu
    Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, China.
  • Gang Xiong
    Department of Information, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
  • Yaoyun Zhang
    Alibaba Damo Academy, 969 West Wen Yi Road, Yu Hang District, Hangzhou, Zhejiang, China.
  • Yingping Yi
    Department of Information, The Second Affiliated Hospital of Nanchang University, Nanchang, China.

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