Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study.

Journal: Journal of medical Internet research
Published Date:

Abstract

BACKGROUND: Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction models with high sensitivity have been developed to anticipate CA, their practical application has been challenging due to a lack of generalization and validation. Additionally, the heterogeneity among patients in different ICU subtypes has not been adequately addressed.

Authors

  • Yun Kwan Kim
    Technology Development, Seers Technology Co. Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea.
  • Won-Doo Seo
    Technology Development, Seers Technology Co. Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea.
  • Sun Jung Lee
    Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
  • Ja Hyung Koo
    Division of Electronics and Electrical Engineering, Dongguk University, 30 Pil-dong-ro, 1-gil, Jung-gu, Seoul 100-715, Korea. koo6190@dongguk.edu.
  • Gyung Chul Kim
    Technology Development, Seers Technology Co. Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea.
  • Hee Seok Song
    Technology Development, Seers Technology Co. Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea.
  • Minji Lee