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

Machine Learning (1465) Artificial Intelligence (1371) Clinical Information and Decision Making (1407) Electronic Health Records (1053)

Published on in Vol 10, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37689, first published .
Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning–Based Approach

Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning–Based Approach

Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning–Based Approach

Authors of this article:

Donghun Yang1, 2 Author Orcid Image ;   Jimin Kim3 Author Orcid Image ;   Junsang Yoo4 Author Orcid Image ;   Won Chul Cha4, 5 Author Orcid Image ;   Hyojung Paik2, 3 Author Orcid Image
Article Authors Cited by (3) Tweetations (4) Metrics

Donghun Yang   1, 2 * , MSc ;   Jimin Kim   3 * , PhD ;   Junsang Yoo   4 , PhD ;   Won Chul Cha   4, 5 , MD, PhD ;   Hyojung Paik   2, 3 , PhD

1 AI Technology Research Center, Division of S&T Digital Convergence, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea

2 Department of Data and High Performance Computing Science, University of Science and Technology, Daejeon, Republic of Korea

3 Center for Supercomputing Applications, Division of National Supercomputing, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea

4 Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea

5 Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Hyojung Paik, PhD
  • Center for Supercomputing Applications
  • Division of National Supercomputing
  • Korea Institute of Science and Technology Information
  • 245 Daehak-ro
  • Yuseong-Gu
  • Daejeon, 34141
  • Republic of Korea
  • Phone: 82 428690791
  • Email: hyojungpaik@gmail.com