An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false-alarm rates. We propose a deep learning-based early warning...

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Podrobná bibliografia
Vydané v:	Journal of the American Heart Association Ročník 7; číslo 13
Hlavní autori:	Kwon, Joon‐myoung, Lee, Youngnam, Lee, Yeha, Lee, Seungwoo, Park, Jinsik
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England John Wiley and Sons Inc 03.07.2018 Wiley
Predmet:	Adult Aged artificial intelligence cardiac arrest Decision Support Techniques Deep Learning Diagnosis, Computer-Assisted Early Diagnosis Female Heart Arrest - diagnosis Heart Arrest - etiology Heart Arrest - mortality Heart Arrest - therapy Humans Inpatients machine learning Male Middle Aged Original Research Predictive Value of Tests Prognosis rapid response system Reproducibility of Results Resuscitation Retrospective Studies Risk Assessment Risk Factors Seoul Time Factors Vital Signs Seoul deep learning resuscitation rapid response system machine learning cardiac arrest artificial intelligence
ISSN:	2047-9980, 2047-9980
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Popis
Shrnutí:	In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false-alarm rates. We propose a deep learning-based early warning system that shows higher performance than the existing track-and-trigger systems. This retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning-based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning-based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity. An algorithm based on deep learning had high sensitivity and a low false-alarm rate for detection of patients with cardiac arrest in the multicenter study.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Dr Kwon and Mr Youngnam Lee contributed equally to this study.
ISSN:	2047-9980 2047-9980
DOI:	10.1161/JAHA.118.008678