165 259

Cited 1 times in

Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database

 Ji Hoon Kim  ;  Arom Choi  ;  Min Joung Kim  ;  Heejung Hyun  ;  Sunhee Kim  ;  Hyuk-Jae Chang 
 SCIENTIFIC REPORTS, Vol.12(1) : 21797, 2022-12 
Journal Title
Issue Date
Emergency Service, Hospital ; Heart Arrest* ; Hospitals ; Humans ; Machine Learning ; Retrospective Studies
In this retrospective observational study, we aimed to develop a machine-learning model using data obtained at the prehospital stage to predict in-hospital cardiac arrest in the emergency department (ED) of patients transferred via emergency medical services. The dataset was constructed by attaching the prehospital information from the National Fire Agency and hospital factors to data from the National Emergency Department Information System. Machine-learning models were developed using patient variables, with and without hospital factors. We validated model performance and used the SHapley Additive exPlanation model interpretation. In-hospital cardiac arrest occurred in 5431 of the 1,350,693 patients (0.4%). The extreme gradient boosting model showed the best performance with area under receiver operating curve of 0.9267 when incorporating the hospital factor. Oxygen supply, age, oxygen saturation, systolic blood pressure, the number of ED beds, ED occupancy, and pulse rate were the most influential variables, in that order. ED occupancy and in-hospital cardiac arrest occurrence were positively correlated, and the impact of ED occupancy appeared greater in small hospitals. The machine-learning predictive model using the integrated information acquired in the prehospital stage effectively predicted in-hospital cardiac arrest in the ED and can contribute to the efficient operation of emergency medical systems.
Files in This Item:
T202300112.pdf Download
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Emergency Medicine (응급의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Min Joung(김민정) ORCID logo https://orcid.org/0000-0003-1634-5209
Kim, Ji Hoon(김지훈) ORCID logo https://orcid.org/0000-0002-0070-9568
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
Choi, Arom(최아롬)
사서에게 알리기


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.