Cargando…
Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models
Background: Early prediction of the clinical outcome of patients with sepsis is of great significance and can guide treatment and reduce the mortality of patients. However, it is clinically difficult for clinicians. Methods: A total of 2,224 patients with sepsis were involved over a 3-year period (2...
Autores principales: | Su, Longxiang, Xu, Zheng, Chang, Fengxiang, Ma, Yingying, Liu, Shengjun, Jiang, Huizhen, Wang, Hao, Li, Dongkai, Chen, Huan, Zhou, Xiang, Hong, Na, Zhu, Weiguo, Long, Yun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8288021/ https://www.ncbi.nlm.nih.gov/pubmed/34291058 http://dx.doi.org/10.3389/fmed.2021.664966 |
Ejemplares similares
-
Evaluation of the updated “Candida score” with Sepsis 3.0 criteria in critically ill patients
por: Li, Dongkai, et al.
Publicado: (2020) -
Noninvasive Real-Time Mortality Prediction in Intensive Care Units Based on Gradient Boosting Method: Model Development and Validation Study
por: Jiang, Huizhen, et al.
Publicado: (2021) -
B-Type Natriuretic Peptide: A Predictor for Mortality, Intensive Care Unit Length of Stay, and Hospital Length of Stay in Patients With Resolving Sepsis
por: Singh, Harsimar, et al.
Publicado: (2017) -
Validating the APACHE IV score in predicting length of stay in the intensive care unit among patients with sepsis
por: Zangmo, Kinley, et al.
Publicado: (2023) -
Effect of Troponin I Elevation on Duration of Mechanical Ventilation and Length of Intensive Care Unit Stay in Patients With Sepsis
por: Abdalla, Mohammed, et al.
Publicado: (2019)