Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study
BackgroundCardiac arrest (CA) is the leading cause of death in critically ill patients. Clinical research has shown that early identification of CA reduces mortality. Algorithms capable of predicting CA with high sensitivity have been developed using multivariate time series...
Main Authors: | Yun Kwan Kim, Ja Hyung Koo, Sun Jung Lee, Hee Seok Song, Minji Lee |
---|---|
Format: | Article |
Language: | English |
Published: |
JMIR Publications
2023-12-01
|
Series: | Journal of Medical Internet Research |
Online Access: | https://www.jmir.org/2023/1/e48244 |
Similar Items
-
Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study
by: Yun Kwan Kim, et al.
Published: (2024-09-01) -
Correction: Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study
by: Yun Kwan Kim, et al.
Published: (2024-10-01) -
Explainable and efficient deep early warning system for cardiac arrest prediction from electronic health records
by: Qinhua Tang, et al.
Published: (2022-07-01) -
Machine Learning-Based Cardiac Arrest Prediction for Early Warning System
by: Minsu Chae, et al.
Published: (2022-06-01) -
Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review
by: Asma Alamgir, et al.
Published: (2021-12-01)