Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU
Abstract Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-ba...
Main Authors: | Hyeonhoon Lee, Hyun-Lim Yang, Ho Geol Ryu, Chul-Woo Jung, Youn Joung Cho, Soo Bin Yoon, Hyun-Kyu Yoon, Hyung-Chul Lee |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-11-01
|
Series: | npj Digital Medicine |
Online Access: | https://doi.org/10.1038/s41746-023-00960-2 |
Similar Items
-
Predicting optimal endotracheal tube size and depth in pediatric patients using demographic data and machine learning techniques
by: Hyeonsik Kim, et al.
Published: (2023-12-01) -
Changes in histopathology and tumor necrosis factor-α levels in the hearts of rats following asphyxial cardiac arrest
by: Jung Hoon Lee, et al.
Published: (2017-09-01) -
Development and validation of a reinforcement learning model for ventilation control during emergence from general anesthesia
by: Hyeonhoon Lee, et al.
Published: (2023-08-01) -
Direct Transport to Cardiac Arrest Center and Survival Outcomes after Out-of-Hospital Cardiac Arrest by Urbanization Level
by: Eujene Jung, et al.
Published: (2022-02-01) -
APACHE II Score Immediately after Cardiac Arrest as a Predictor of Good Neurological Outcome in Out-of-Hospital Cardiac Arrest Patients Receiving Targeted Temperature Management
by: Sang-Il Kim, et al.
Published: (2018-05-01)