Cargando…
Using machine learning methods to predict 28-day mortality in patients with hepatic encephalopathy
BACKGROUND: Hepatic encephalopathy (HE) is associated with marked increases in morbidity and mortality for cirrhosis patients. This study aimed to develop and validate machine learning (ML) models to predict 28-day mortality for patients with HE. METHODS: A retrospective cohort study was conducted i...
Autores principales: | Zhang, Zhe, Wang, Jian, Han, Wei, Zhao, Li |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10077693/ https://www.ncbi.nlm.nih.gov/pubmed/37024814 http://dx.doi.org/10.1186/s12876-023-02753-z |
Ejemplares similares
-
Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy
por: Peng, Liwei, et al.
Publicado: (2022) -
Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer
por: Guo, Chunxia, et al.
Publicado: (2023) -
Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury
por: Yang, Jijun, et al.
Publicado: (2023) -
Neutrophil-to-lymphocyte ratio predicts 30-, 90-, and 180-day readmissions of patients with hepatic encephalopathy
por: Zhang, Lin, et al.
Publicado: (2023) -
Trauma Early Mortality Prediction Tool (TEMPT) for assessing 28-day mortality
por: Kunitake, Ryan C, et al.
Publicado: (2018)