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

Clinical Information and Decision Making (1340) Clinical Informatics (919) Decision Support for Health Professionals (1147) Machine Learning (1401) Artificial Intelligence (1216) Cardiology (134) Cardiac Disease Management (81)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48244, first published .
Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study

Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study

Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study

Authors of this article:

Yun Kwan Kim1, 2 Author Orcid Image ;   Ja Hyung Koo1 Author Orcid Image ;   Sun Jung Lee1 Author Orcid Image ;   Hee Seok Song1 Author Orcid Image ;   Minji Lee3 Author Orcid Image
Article Authors Cited by (2) Tweetations (6) Metrics

Yun Kwan Kim   1, 2 , MSc ;   Ja Hyung Koo   1 , PhD ;   Sun Jung Lee   1 , PhD ;   Hee Seok Song   1 , MSE ;   Minji Lee   3 , PhD

1 Department of Research and Development, Seers Technology Co, Ltd, Pyeongtaek, Republic of Korea

2 Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea

3 Department of Biomedical Software Engineering, The Catholic University of Korea, Gyeonggi, Republic of Korea

Corresponding Author:

  • Minji Lee, PhD
  • Department of Biomedical Software Engineering
  • The Catholic University of Korea
  • 43, Jibong-ro, Bucheon
  • Gyeonggi, 14662
  • Republic of Korea
  • Phone: 82 2-2164-4364
  • Email: minjilee@catholic.ac.kr