Identification of out-of-hospital cardiac arrest clusters using unsupervised learning
- 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% respectively. 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.
- All Author(s)
- H. J. Moon
; Y. J. Shin
; Y. S. Cho
- Issued Date
- 2022
- Type
- Article
- Keyword
- Out-of-hospital cardiac arrest; Emergency medical services; Artificial intelligence
- Publisher
- Elsevier
- ISSN
- 0735-6757
- Citation Title
- The American Journal of Emergency Medicine
- Citation Volume
- 62
- Citation Start Page
- 41
- Citation End Page
- 48
- Language(ISO)
- eng
- DOI
- 10.1016/j.ajem.2022.09.035
- URI
- http://schca-ir.schmc.ac.kr/handle/2022.oak/1185
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