Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning

Title : Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning
Journal : Diagnostics
Authors : Minsu Chae, Sangwook Han, Hyowook Gil, Namjun Cho, Hwamin Lee
Corresponding author : Hwamin Lee
DOI : https://doi.org/10.3390/diagnostics11071255

Abstract
Sudden cardiac arrest can leave serious brain damage or lead to death, so it is very important to predict before a cardiac arrest occurs. However, early warning score systems including the National Early Warning Score, are associated with low sensitivity and false positives. We applied shallow and deep learning to predict cardiac arrest to overcome these limitations. We evaluated the performance of the Synthetic Minority Oversampling Technique Ratio. We evaluated the performance using a Decision Tree, a Random Forest, Logistic Regression, Long Short-Term Memory model, Gated Recurrent Unit model, and LSTM–GRU hybrid models. Our proposed Logistic Regression demonstrated a higher positive predictive value and sensitivity than traditional early warning systems.

Acknowledgments
This research was supported by the Bio and Medical Technology Development Program and Basic Science Research Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. NRF-2019M3E5D1A02069073 & NRF-2021R1A2C1009290) and Soonchunhyang University Research Fund.