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Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database
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. Pat...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9784963/ https://www.ncbi.nlm.nih.gov/pubmed/36547427 http://dx.doi.org/10.3390/jcdd9120430 |
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author | Choi, Arom Kim, Min Joung Sung, Ji Min Kim, Sunhee Lee, Jayoung Hyun, Heejung Kim, Hyeon Chang Kim, Ji Hoon Chang, Hyuk-Jae |
author_facet | Choi, Arom Kim, Min Joung Sung, Ji Min Kim, Sunhee Lee, Jayoung Hyun, Heejung Kim, Hyeon Chang Kim, Ji Hoon Chang, Hyuk-Jae |
author_sort | Choi, Arom |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9784963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97849632022-12-24 Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database Choi, Arom Kim, Min Joung Sung, Ji Min Kim, Sunhee Lee, Jayoung Hyun, Heejung Kim, Hyeon Chang Kim, Ji Hoon Chang, Hyuk-Jae J Cardiovasc Dev Dis Article 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. MDPI 2022-12-02 /pmc/articles/PMC9784963/ /pubmed/36547427 http://dx.doi.org/10.3390/jcdd9120430 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Choi, Arom Kim, Min Joung Sung, Ji Min Kim, Sunhee Lee, Jayoung Hyun, Heejung Kim, Hyeon Chang Kim, Ji Hoon Chang, Hyuk-Jae Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database |
title | Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database |
title_full | Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database |
title_fullStr | Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database |
title_full_unstemmed | Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database |
title_short | Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database |
title_sort | development of prediction models for acute myocardial infarction at prehospital stage with machine learning based on a nationwide database |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9784963/ https://www.ncbi.nlm.nih.gov/pubmed/36547427 http://dx.doi.org/10.3390/jcdd9120430 |
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