Prediction of Critical Care Outcome for Adult Patients Presenting to Emergency Department Using Initial Triage Information: An XGBoost Algorithm Analysis

BACKGROUND: The emergency department (ED) triage system to classify and prioritize patients from high risk to less urgent continues to be a challenge. OBJECTIVE: This study, comprising 80,433 patients, aims to develop a machine learning algorithm prediction model of critical care outcomes for adult...

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Detalles Bibliográficos
Autores principales: Yun, Hyoungju, Choi, Jinwook, Park, Jeong Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491120/
https://www.ncbi.nlm.nih.gov/pubmed/34346889
http://dx.doi.org/10.2196/30770

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491120/
https://www.ncbi.nlm.nih.gov/pubmed/34346889
http://dx.doi.org/10.2196/30770

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