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

10.1186/cc11396

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Bibliographic Details
Main Authors:	Hock Ong, M.E, Lee Ng, C.H, Goh, K, Liu, N, Koh, Z.X, Shahidah, N, Zhang, T.T, Fook-Chong, S, Lin, Z
Other Authors:	DUKE-NUS MEDICAL SCHOOL
Format:	Article
Published:	2020
Subjects:	adult aged article clinical trial controlled study critically ill patient emergency health service female heart arrest heart rate variability human machine learning major clinical study male Modified Early Warning Score predictive value priority journal scoring system sensitivity and specificity tertiary health care treatment outcome artificial intelligence cohort analysis comparative study critical illness heart rate middle aged pathophysiology physiology prospective study severity of illness index standards Aged Artificial Intelligence Cohort Studies Critical Illness Emergency Service, Hospital Female Heart Arrest Heart Rate Humans Male Middle Aged Predictive Value of Tests Prospective Studies Severity of Illness Index
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/175334
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Institution:	National University of Singapore

Description
Summary:	10.1186/cc11396