Cargando…
Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU
BACKGROUND: Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predict the risk of hospital death of patients with sep...
Autores principales: | Kong, Guilan, Lin, Ke, Hu, Yonghua |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531110/ https://www.ncbi.nlm.nih.gov/pubmed/33008381 http://dx.doi.org/10.1186/s12911-020-01271-2 |
Ejemplares similares
-
Predicting Prolonged Length of ICU Stay through Machine Learning
por: Wu, Jingyi, et al.
Publicado: (2021) -
Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis
por: Zeng, Zhixuan, et al.
Publicado: (2021) -
Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree
por: Li, Ke, et al.
Publicado: (2021) -
A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients
por: Wang, Dong, et al.
Publicado: (2021) -
Predicting open wound mortality in the ICU using machine learning
por: Akiki, Ronald K., et al.
Publicado: (2021)