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

Clinical Information and Decision Making (1655) Machine Learning (1737) Artificial Intelligence (1739) Clinical Informatics (1162) Cardiac Diagnostics (21) Myocardial Infarction (Heart Attack) (50)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62890, first published .
Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study

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

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

Authors of this article:

Yun Kwan Kim1, 2 Author Orcid Image ;   Won-Doo Seo1 Author Orcid Image ;   Sun Jung Lee1 Author Orcid Image ;   Ja Hyung Koo1 Author Orcid Image ;   Gyung Chul Kim1 Author Orcid Image ;   Hee Seok Song1 Author Orcid Image ;   Minji Lee3 Author Orcid Image
Article Authors Cited by (4) Tweetations (1) Metrics

Journals

  1. Antra , Kumar U, Loria G, Nayak Y. Beyond the alarm: proactive predictions for cardiac arrest incidents in hospitals using Interpretable machine learning models. Journal of the Operational Research Society 2024:1 View
  2. Sang W, Ma J, Zhang X, Wu S, Pan C, Zheng J, Zheng W, Yuan Q, Zhang J, Ma J, Xu F. Early prediction cardiac arrest in intensive care units: the value of laboratory indicator trends. World Journal of Emergency Medicine 2025;16(1):67 View
  3. Tang H, Qu M, Xin M, He T, Al-Nimer M. Association of mean corpuscular volume with 28-day mortality in sepsis patients: A retrospective cohort study using eICU data. PLOS ONE 2025;20(4):e0321213 View
  4. Al-Ansari A, Nejad F, Al-Nasr R, Prithula J, Rahman T, Hasan A, Chowdhury M, Alam M. Predicting ICU Mortality Among Septic Patients Using Machine Learning Technique. Journal of Clinical Medicine 2025;14(10):3495 View