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

id	sg-nus-scholar.10635-175334
record_format	dspace
spelling	sg-nus-scholar.10635-1753342023-09-06T09:02:57Z 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 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 DUKE-NUS MEDICAL SCHOOL 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 emergency health service heart arrest 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 10.1186/cc11396 Critical Care 16 3 R108 2020-09-09T09:45:43Z 2020-09-09T09:45:43Z 2012 Article 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 (2012). 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. Critical Care 16 (3) : R108. ScholarBank@NUS Repository. https://doi.org/10.1186/cc11396 1364-8535 https://scholarbank.nus.edu.sg/handle/10635/175334 Unpaywall 20200831
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	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 emergency health service heart arrest 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
spellingShingle	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 emergency health service heart arrest 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 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 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
description	10.1186/cc11396
author2	DUKE-NUS MEDICAL SCHOOL
author_facet	DUKE-NUS MEDICAL SCHOOL 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
format	Article
author	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
author_sort	Hock Ong, M.E
title	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
title_short	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
title_full	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
title_fullStr	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
title_full_unstemmed	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
title_sort	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
publishDate	2020
url	https://scholarbank.nus.edu.sg/handle/10635/175334
_version_	1778169903628419072

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

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