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

• 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: 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
• Institution: Nanyang Technological University

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...