Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning.
Comput Inform Nurs
; 42(5): 388-395, 2024 May 01.
Article
en En
| MEDLINE
| ID: mdl-39248449
ABSTRACT
As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Paro Cardíaco Extrahospitalario
/
Aprendizaje Automático
Límite:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
País/Región como asunto:
Asia
Idioma:
En
Revista:
Comput Inform Nurs
Asunto de la revista:
ENFERMAGEM
/
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Corea del Sur
Pais de publicación:
Estados Unidos