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

Machine Learning (1501) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210) Myocardial Infarction (Heart Attack) (45) Intensive Care Unit (ICU) (114)

Published on in Vol 8, No 3 (2020): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16349, first published .
Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study

Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study

Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study

Authors of this article:

Junetae Kim1, 2, 3 Author Orcid Image ;   Yu Rang Park4 Author Orcid Image ;   Jeong Hoon Lee4 Author Orcid Image ;   Jae-Ho Lee5, 6 Author Orcid Image ;   Young-Hak Kim7, 8, 9, 10 Author Orcid Image ;   Jin Won Huh11 Author Orcid Image
Article Authors Cited by (14) Tweetations (2) Metrics

Journals

  1. Kim H, Kim Y, Park Y. Interpretable Conditional Recurrent Neural Network for Weight Change Prediction: Algorithm Development and Validation Study. JMIR mHealth and uHealth 2021;9(3):e22183 View
  2. Li R, Yin C, Yang S, Qian B, Zhang P. Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach. Journal of Medical Internet Research 2020;22(9):e20645 View
  3. Yijing L, Wenyu Y, Kang Y, Shengyu Z, Xianliang H, Xingliang J, Cheng W, Zehui S, Mengxing L. Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring. Computer Methods and Programs in Biomedicine 2022;214:106568 View
  4. Sung M, Hahn S, Han C, Lee J, Lee J, Yoo J, Heo J, Kim Y, Chung K. Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study. JMIR Medical Informatics 2021;9(11):e26426 View
  5. Do T, Yang H, Lee G, Kim S, Kho B. Rapid Response System Based on Graph Attention Network for Predicting In-Hospital Clinical Deterioration. IEEE Access 2023;11:29091 View
  6. Ruan X, Wang L, Thongprayoon C, Cheungpasitporn W, Liu H. GRU-D-Weibull: A novel real-time individualized endpoint prediction. Artificial Intelligence in Medicine 2023;146:102696 View
  7. Holmström L, Zhang F, Ouyang D, Dey D, Slomka P, Chugh S. Artificial Intelligence in Ventricular Arrhythmias and Sudden Death. Arrhythmia & Electrophysiology Review 2023;12 View
  8. Rajapaksha L, Vidanagamachchi S, Gunawardena S, Thambawita V. An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation. BioMedInformatics 2023;4(1):34 View
  9. Sutanto H. Transforming clinical cardiology through neural networks and deep learning: A guide for clinicians. Current Problems in Cardiology 2024;49(4):102454 View
  10. Do T, Yang H, Kim S, Kho B, Park J. Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network. International Journal of Medical Informatics 2025;195:105745 View

Books/Policy Documents

  1. Báez E, Vallejos S, Pisarello M. IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering. View
  2. Babu C, S. S. Clinical Practice and Unmet Challenges in AI-Enhanced Healthcare Systems. View