This paper is in the following e-collection/theme issue:

Tools, Programs and Algorithms (275) Data Science (278) Articles* (13) Big Data (245)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38590, first published .
Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach

Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach

Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach

Authors of this article:

Xiaojie Chen1 Author Orcid Image ;   Han Chen2 Author Orcid Image ;   Shan Nan1 Author Orcid Image ;   Xiangtian Kong3 Author Orcid Image ;   Huilong Duan1, 4 Author Orcid Image ;   Haiyan Zhu2, 5 Author Orcid Image
Article Authors Cited by (6) Tweetations (3) Metrics

Xiaojie Chen   1 * , MS ;   Han Chen   2 * , PhD ;   Shan Nan   1 , PhD ;   Xiangtian Kong   3 , PhD ;   Huilong Duan   1, 4 , PhD ;   Haiyan Zhu   2, 5 , PhD

1 Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China

2 Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China

3 IMWare, Wuhan, China

4 College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China

5 First Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China

*these authors contributed equally

Corresponding Author:

  • Haiyan Zhu, PhD
  • First Medical Center of Chinese People's Liberation Army General Hospital
  • 28 Fuxing Road, Haidian District
  • Beijing, 100037
  • China
  • Phone: 86 13521361644
  • Email: xiaoyanzibj301@163.com