A Prediction of in-Hospital Cardiac Arrest Risk Scoring Based on Machine Learning

Minsu Chae (1), Hwamin Lee (2)
(1) Department of Medical Informatics, College of Medicine, Korea University, Seoul, 02841, Republic of Korea
(2) Department of Medical Informatics, College of Medicine, Korea University, Seoul, 02841, Republic of Korea
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How to cite (IJASEIT) :
Chae, Minsu, and Hwamin Lee. “A Prediction of in-Hospital Cardiac Arrest Risk Scoring Based on Machine Learning”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 3, June 2023, pp. 895-00, doi:10.18517/ijaseit.13.3.17343.
According to the Korea Disease Control and Prevention Agency (KCDC), 591 out of 33,402 cardiac arrests in 2021 occurred in hospitals. A recent study shows that the golden time to detect a cardiac arrest is less than three minutes. It means early detection of cardiac arrest is important. However, early warning systems predict cardiac arrest with low precision and recall. We research data from ICU patients aged 19 and older who were hospitalized at the Korea University Anam Hospital from 2021 to 2022. We grouped patients with similar characteristics based on clustering the selection, such as in prospective studies. We clustered the training data by window sliding age, SBP, DBP, BT, RR, BP, and BT over 8 hours. We applied a long short-term memory (LSTM) model, a recurrent gated model (GRU) model, and a self-attention-based LSTM model. Instead of linear regression, we used multiple classifications to predict values from 0 to 100. We assign weight to each score. We proposed a cardiac arrest risk score and developed a prediction model for cardiac arrest risk score using ICU patients from the Korea University Anam Hospital. We used the cardiac arrest risk score to predict cardiac arrest within 8 hours, 24 hours, and 72 hours. We evaluated the predicted cardiac arrest risk score as 0 below the threshold and 1 above the threshold. Our proposed GRU model shows 0.11% precision and 94.34% recall.

M. J. Holmberg et al., "Annual incidence of adult and pediatric in-hospital cardiac arrest in the United States." Circulation: Cardiovascular Quality and Outcomes, vol. 12, no. 7, e005580, Jul. 2019.

J. Kim, J. Jeong, and S. Kweon. "Incidences of Out-of-hospital Sudden Cardiac Arrest in the Republic of Korea, 2021." Public Health Weekly Report., CheongJu, the Republic of Korea, Dec. 2022.

National Injury Information Portal , https://www.kdca.go.kr/injury/biz/injury/recsroom/rawDta/rawDtaDwldMain.do

J. Kang et al., "Association between time to defibrillation and neurologic outcome in patients with in-hospital cardiac arrest." The American Journal of the Medical Sciences, vol. 358, no. 2, pp. 143-148, Aug. 2019

J. Kwon et al., "An algorithm based on deep learning for predicting in”hospital cardiac arrest." Journal of the American Heart Association, vol. 7, no. 13, e008678, Jul. 2018.

J. H. Ví¤hí¤talo et al., "Association of silent myocardial infarction and sudden cardiac death." JAMA cardiology, vol. 4, no. 8, pp. 796-802, Jul. 2019.

T. T. Wu et al., "Machine learning for early prediction of in”hospital cardiac arrest in patients with acute coronary syndromes." Clinical Cardiology, vol. 44, no. 3, pp. 349-356, Feb. 2021.

S. L. Javan et al,. "An intelligent warning model for early prediction of cardiac arrest in sepsis patients." Computer methods and programs in biomedicine, vol. 178, pp-47-58, Sep. 2019.

S. Hong et al., "Prediction of cardiac arrest in the emergency department based on machine learning and sequential characteristics: model development and retrospective clinical validation study." JMIR medical informatics, vol. 8, no. 8, e15932, Aug. 2020.

D. Jang et al., "Developing neural network models for early detection of cardiac arrest in emergency department." The American journal of emergency medicine, vol. 38, no.1, pp. 43-49, Jan. 2020.

B. R. Matam, D. Heathe, and L. David, "Machine learning based framework to predict cardiac arrests in a paediatric intensive care unit: Prediction of cardiac arrests." Journal of clinical monitoring and computing, vol. 33, pp. 713-724, Aug. 2019

J. Kim et al., "Development of a real-time risk prediction model for in-hospital cardiac arrest in critically ill patients using deep learning: retrospective study." JMIR Medical Informatics, vol. 8, no. 3, e16349, Mar. 2020.

L. Yijing et al., "Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring." Computer Methods and Programs in Biomedicine, vol. 214, 106568., Feb. 2022

L. Ibrahim et al., "Explainable prediction of acute myocardial infarction using machine learning and shapley values." IEEE Access, vol. 8, pp. 210410-210417, Nov. 2020.

A. Sbrollini et al., "Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach." BioMedical Engineering OnLine, vol. 18, Feb. 2019.

R. Ueno et al., "Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study." PloS one, vol.15, no. 7, e0235835, Jun. 2020.

L. Nan et al., "Development and validation of an interpretable prehospital return of spontaneous circulation (P-ROSC) score for patients with out-of-hospital cardiac arrest using machine learning: a retrospective study." Eclinicalmedicine, vol. 48, Jun. 2022.

A. Shah et al., "Smart cardiac framework for an early detection of cardiac arrest condition and risk." Frontiers in Public Health, vol. 9. 762303, Oct. 2021.

A. Kachhawa, and J. Hitt, "An Intelligent System for Early Prediction of Cardiovascular Disease using Machine Learning." Journal of Student Research, vol. 11, no. 3, Aug. 2022.

N. P. Desai et al., "A Comparative Assessment Study on Machine Learning Classifiers for Cardiac Arrest Diagnosis and Prediction." In International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES, Chennai, India, 2021, pp.. IEEE, 2021.

R. Karthikeyan et al., "Cardiac Arrest Prediction using Machine Learning Algorithms." Journal of Physics: Conference Series. vol. 1964, no. 6. Jul 2021.

S. Baral et al., "A novel solution of using deep learning for early prediction cardiac arrest in Sepsis patient: enhanced bidirectional long short-term memory (LSTM)." Multimedia Tools and Applications, vol. 80, pp. 32639-32664, Aug. 2021.

J. Kim et al., "Predicting cardiac arrest and respiratory failure using feasible artificial intelligence with simple trajectories of patient data." Journal of clinical medicine, vol. 8, no. 9, 1336, Aug. 2019.

Y. Chiu et al., "Logistic early warning scores to predict death, cardiac arrest or unplanned intensive care unit re”admission after cardiac surgery." Anaesthesia, vol. 75., no. 2. pp. 162-170, 2020

S. L. Javan, and M. M. Sepehri, "A predictive framework in healthcare: Case study on cardiac arrest prediction." Artificial Intelligence in Medicine, vol. 117, 102099, Jul. 2021.

D. Florence, B. Wulfran, and C. Alain, "Cardiac arrest: prediction models in the early phase of hospitalization." Current opinion in critical care, vol. 25, no. 3, pp. 204-210, Jun. 2019.

F. Pedregosa et al., "Scikit-learn: Machine learning in Python." the Journal of machine Learning research, vol 12 (2011): 2825-2830.

N. V. Chawla et al. "SMOTE: synthetic minority over-sampling technique." Journal of artificial intelligence research, vol 16, pp. 321-357, Jun. 2002 16 (2002): 321-357.

S. Hochreiter, and J. Schmidhuber, "Long short-term memory." Neural computation, vol. 9. no.8 pp. 1735-1780, Nov. 1997.

K. Cho et al., "Learning phrase representations using RNN encoder-decoder for statistical machine translation." arXiv preprint arXiv:1406.1078, 2014.

A. Vaswani, et al. "Attention is all you need." Advances in neural information processing systems, 30, 2017.

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