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Article

Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database

by
Arom Choi
1,†,
Min Joung Kim
1,†,
Ji Min Sung
2,
Sunhee Kim
3,
Jayoung Lee
4,
Heejung Hyun
4,
Hyeon Chang Kim
5,
Ji Hoon Kim
1,* and
Hyuk-Jae Chang
2 on behalf of the Connected Network for EMS Comprehensive Technical Support Using Artificial Intelligence Investigators
1
Department of Emergency Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
2
Department of Cardiology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
3
CONNECT-AI Research Center, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
4
AITRICS, Seoul 06627, Republic of Korea
5
Department of Preventive Medicine and Public Health, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Cardiovasc. Dev. Dis. 2022, 9(12), 430; https://doi.org/10.3390/jcdd9120430
Submission received: 18 October 2022 / Revised: 22 November 2022 / Accepted: 28 November 2022 / Published: 2 December 2022
(This article belongs to the Special Issue Models and Methods for Computational Cardiology)
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Abstract

Models for predicting acute myocardial infarction (AMI) at the prehospital stage were developed and their efficacy compared, based on variables identified from a nationwide systematic emergency medical service (EMS) registry using conventional statistical methods and machine learning algorithms. Patients in the EMS cardiovascular registry aged >15 years who were transferred from the public EMS to emergency departments in Korea from January 2016 to December 2018 were enrolled. Two datasets were constructed according to the hierarchical structure of the registry. A total of 184,577 patients (Dataset 1) were included in the final analysis. Among them, 72,439 patients (Dataset 2) were suspected to have AMI at prehospital stage. Between the models derived using the conventional logistic regression method, the B-type model incorporated AMI-specific variables from the A-type model and exhibited a superior discriminative ability (p = 0.02). The models that used extreme gradient boosting and a multilayer perceptron yielded a higher predictive performance than the conventional logistic regression-based models for analyses that used both datasets. Each machine learning algorithm yielded different classification lists of the 10 most important features. Therefore, prediction models that use nationwide prehospital data and are developed with appropriate structures can improve the identification of patients who require timely AMI management.
Keywords: acute myocardial infarction; prediction; machine learning; nationwide prehospital record acute myocardial infarction; prediction; machine learning; nationwide prehospital record

Share and Cite

MDPI and ACS Style

Choi, A.; Kim, M.J.; Sung, J.M.; Kim, S.; Lee, J.; Hyun, H.; Kim, H.C.; Kim, J.H.; Chang, H.-J., on behalf of the Connected Network for EMS Comprehensive Technical Support Using Artificial Intelligence Investigators. Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database. J. Cardiovasc. Dev. Dis. 2022, 9, 430. https://doi.org/10.3390/jcdd9120430

AMA Style

Choi A, Kim MJ, Sung JM, Kim S, Lee J, Hyun H, Kim HC, Kim JH, Chang H-J on behalf of the Connected Network for EMS Comprehensive Technical Support Using Artificial Intelligence Investigators. Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database. Journal of Cardiovascular Development and Disease. 2022; 9(12):430. https://doi.org/10.3390/jcdd9120430

Chicago/Turabian Style

Choi, Arom, Min Joung Kim, Ji Min Sung, Sunhee Kim, Jayoung Lee, Heejung Hyun, Hyeon Chang Kim, Ji Hoon Kim, and Hyuk-Jae Chang on behalf of the Connected Network for EMS Comprehensive Technical Support Using Artificial Intelligence Investigators. 2022. "Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database" Journal of Cardiovascular Development and Disease 9, no. 12: 430. https://doi.org/10.3390/jcdd9120430

APA Style

Choi, A., Kim, M. J., Sung, J. M., Kim, S., Lee, J., Hyun, H., Kim, H. C., Kim, J. H., & Chang, H.-J., on behalf of the Connected Network for EMS Comprehensive Technical Support Using Artificial Intelligence Investigators. (2022). Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database. Journal of Cardiovascular Development and Disease, 9(12), 430. https://doi.org/10.3390/jcdd9120430

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