Identification of out-of-hospital cardiac arrest clusters using unsupervised learning
DC Field | Value | Language |
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dc.contributor.author | Moon, Hyung Jun | - |
dc.contributor.author | Shin, Yong Jin | - |
dc.contributor.author | Cho, Young Soon | - |
dc.date.accessioned | 2022-11-29T06:40:37Z | - |
dc.date.available | 2022-11-29T06:40:37Z | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 0735-6757 | - |
dc.identifier.issn | 1532-8171 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/21832 | - |
dc.description.abstract | Aim: Out-of-hospital cardiac arrest (OHCA) is a leading cause of death, and research has identified limitations in analyzing the factors related to the incidence of cardiac arrest and the frequency of bystander cardiopulmonary resuscitation. This study conducts a cluster analysis of the correlation between location-related factors and the outcome of patients with OHCA using two machine learning methods: variational autoencoder (VAE) and the Dirichlet process mixture model (DPMM). Methods: Using the prospectively collected Smart Advanced Life Support registry in South Korea between August 2015 and December 2018, a secondary retrospective data analysis was performed on patients with OHCA with a presumed cause of cardiac arrest in adults of 18 years or older. VAE and DPMM were used to create clusters to determine groups with a common nature among those with OHCA. Results: Among 5876 OHCA cases, 1510 patients were enrolled in the final analysis. Decision tree-based models, which have an accuracy of 95.36%, were also used to interpret the characteristics of clusters. A total of 8 clusters that had similar spatial characteristics were identified using DPMM and VAE. Among the generated clusters, the averages of the four clusters that exhibited a high survival to discharge rate and a favorable neurological outcome were 9.6% and 6.1%, and the averages of the four clusters that exhibited a low outcome were 5.1% and 3.5% respec-tively. In the decision tree-based models, the most important feature that could affect the prognosis of an OHCA patient was being transferred to a higher-level emergency center. Conclusion: This methodology can facilitate the development of a regionalization strategy that can improve the survival rate of cardiac arrest patients in different regions. (c) 2022 Published by Elsevier Inc. | - |
dc.format.extent | 8 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | W. B. Saunders Co., Ltd. | - |
dc.title | Identification of out-of-hospital cardiac arrest clusters using unsupervised learning | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1016/j.ajem.2022.09.035 | - |
dc.identifier.scopusid | 2-s2.0-85139731754 | - |
dc.identifier.wosid | 000876479000003 | - |
dc.identifier.bibliographicCitation | American Journal of Emergency Medicine, v.62, pp 41 - 48 | - |
dc.citation.title | American Journal of Emergency Medicine | - |
dc.citation.volume | 62 | - |
dc.citation.startPage | 41 | - |
dc.citation.endPage | 48 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Emergency Medicine | - |
dc.relation.journalWebOfScienceCategory | Emergency Medicine | - |
dc.subject.keywordPlus | AMERICAN-HEART-ASSOCIATION | - |
dc.subject.keywordPlus | RESUSCITATION | - |
dc.subject.keywordPlus | GUIDELINES | - |
dc.subject.keywordAuthor | Out-of-hospital cardiac arrest | - |
dc.subject.keywordAuthor | Emergency medical services | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
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