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

Machine Learning (1370) Myocardial Infarction (Heart Attack) (38)

Published on in Vol 8, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15932, first published .
Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study

Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study

Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study

Authors of this article:

Sungjun Hong1 Author Orcid Image ;   Sungjoo Lee1 Author Orcid Image ;   Jeonghoon Lee1 Author Orcid Image ;   Won Chul Cha1, 2, 3 Author Orcid Image ;   Kyunga Kim1, 4 Author Orcid Image
Article Authors Cited by (19) Tweetations (1) Metrics

Journals

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  2. 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
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  7. Nwanosike E, Sunter W, Ansari M, Merchant H, Conway B, Hasan S. A Real-World Exploration into Clinical Outcomes of Direct Oral Anticoagulant Dosing Regimens in Morbidly Obese Patients Using Data-Driven Approaches. American Journal of Cardiovascular Drugs 2023;23(3):287 View
  8. Choi A, Chung K, Chung S, Lee K, Hyun H, Kim J. Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis. Sensors 2022;22(18):7054 View
  9. Li C, Zhang Z, Ren Y, Nie H, Lei Y, Qiu H, Xu Z, Pu X. Machine learning based early mortality prediction in the emergency department. International Journal of Medical Informatics 2021;155:104570 View
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  11. Hammad H, Rizani K, Rachmadi A, SPN E, Rizani A, Marwansyah M, Wilotono N. Cardiopulmonary Resuscitation Capacity Building Training for High School Students in Martapura, Banjar Regency. International Journal of Community Service Learning 2023;7(1):9 View
  12. Sadegh-Zadeh S, Sakha H, Movahedi S, Fasihi Harandi A, Ghaffari S, Javanshir E, Ali S, Hooshanginezhad Z, Hajizadeh R. Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification. Computers in Biology and Medicine 2023;167:107696 View
  13. 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
  14. Choi A, Choi S, Chung K, Chung H, Song T, Choi B, Kim J. Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department. Scientific Reports 2023;13(1) View
  15. Lee H, Yang H, Ryu H, Jung C, Cho Y, Yoon S, Yoon H, Lee H. Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU. npj Digital Medicine 2023;6(1) View
  16. Kim Y, Koo J, Lee S, Song H, Lee M. Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study. Journal of Medical Internet Research 2023;25:e48244 View

Books/Policy Documents

  1. Woodman R, Mangoni A. Gerontechnology. A Clinical Perspective. View