Cargando…
Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection
BACKGROUND: The key aim of triage in chest pain patients is to identify those with high risk of adverse cardiac events as they require intensive monitoring and early intervention. In this study, we aim to discover the most relevant variables for risk prediction of major adverse cardiac events (MACE)...
Autores principales: | Liu, Nan, Koh, Zhi Xiong, Goh, Junyang, Lin, Zhiping, Haaland, Benjamin, Ting, Boon Ping, Ong, Marcus Eng Hock |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4150554/ https://www.ncbi.nlm.nih.gov/pubmed/25150702 http://dx.doi.org/10.1186/1472-6947-14-75 |
Ejemplares similares
-
Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department
por: Liu, Nan, et al.
Publicado: (2021) -
Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score
por: Ong, Marcus Eng Hock, et al.
Publicado: (2012) -
A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain
por: Heldeweg, Micah Liam Arthur, et al.
Publicado: (2016) -
Heart rate n-variability (HRnV) and its application to risk stratification of chest pain patients in the emergency department
por: Liu, Nan, et al.
Publicado: (2020) -
Combining quick sequential organ failure assessment score with heart rate variability may improve predictive ability for mortality in septic patients at the emergency department
por: Prabhakar, Sumanth Madhusudan, et al.
Publicado: (2019)