Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score

Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score

Introduction: A key aim of triage is to identify those with high risk of cardiac arrest, as they require intensive monitoring, resuscitation facilities, and early intervention. We aim to validate a novel machine learning (ML) score incorporating heart rate variability (HRV) for triage of critically...

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Bibliographic Details
Main Authors: Ong, Marcus Eng Hock., Lee Ng, Christina Hui., Goh, Ken., Liu, Nan., Koh, Zhi Xiong., Shahidah, Nur., Zhang, Tongtong., Fook-Chong, Stephanie., Lin, Zhiping.
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2013
Subjects:
DRNTU::Engineering::Electrical and electronic engineering
Online Access:https://hdl.handle.net/10356/84800
http://hdl.handle.net/10220/10171

Internet

https://hdl.handle.net/10356/84800
http://hdl.handle.net/10220/10171

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