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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number21797
JournalScientific reports
Issue number1
Publication statusPublished - 2022 Dec

Bibliographical note

Funding Information:
This study was supported by a new faculty research seed money grant of Yonsei University College of Medicine for 2021 (6-2021-0140) and a grant from the 2019 IT Promotion fund (Development of AI based Precision Medicine Emergency system) of the government of Korea (Ministry of Science and ICT) (Grant Number: S1015-19-1001).

Publisher Copyright:
© 2022, The Author(s).

All Science Journal Classification (ASJC) codes

  • General


Dive into the research topics of 'Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database'. Together they form a unique fingerprint.

Cite this