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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Detalles Bibliográficos
Autores principales: Ong, Marcus Eng Hock, Lee Ng, Christina Hui, Goh, Ken, Liu, Nan, Koh, Zhi Xiong, Shahidah, Nur, Zhang, Tong Tong, Fook-Chong, Stephanie, Lin, Zhiping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3580666/
https://www.ncbi.nlm.nih.gov/pubmed/22715923
http://dx.doi.org/10.1186/cc11396

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3580666/
https://www.ncbi.nlm.nih.gov/pubmed/22715923
http://dx.doi.org/10.1186/cc11396

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