Machine Learning-Based Cardiac Arrest Prediction for Early Warning System

Minsu Chae, Hyo-Wook Gil, Nam-Jun Cho and Hwamin Lee
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Minsu Chae: Department of Medical Informatics, College of Medicine, Korea University, Seoul 02841, Korea
Hyo-Wook Gil: Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea
Nam-Jun Cho: Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea
Hwamin Lee: Department of Medical Informatics, College of Medicine, Korea University, Seoul 02841, Korea

Mathematics, 2022, vol. 10, issue 12, 1-17

Abstract: The early warning system detects early and responds quickly to emergencies in high-risk patients, such as cardiac arrest in hospitalized patients. However, traditional early warning systems have the problem of frequent false alarms due to low positive predictive value and sensitivity. We conducted early prediction research on cardiac arrest using time-series data such as biosignal and laboratory data. To derive the data attributes that affect the occurrence of cardiac arrest, we performed a correlation analysis between the occurrence of cardiac arrest and the biosignal data and laboratory data. To improve the positive predictive value and sensitivity of early cardiac arrest prediction, we evaluated the performance according to the length of the time series of measured biosignal data, laboratory data, and patient data range. We propose a machine learning and deep learning algorithm: the decision tree, random forest, logistic regression, long short-term memory (LSTM), gated recurrent unit (GRU) model, and the LSTM–GRU hybrid model. We evaluated cardiac arrest prediction models. In the case of our proposed LSTM model, the positive predictive value was 85.92% and the sensitivity was 89.70%.

Keywords: cardiac arrest; machine learning; deep learning; early warning system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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