Cargando…
Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis
Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divide...
Autores principales: | Yu, Chenyan, Li, Yao, Yin, Minyue, Gao, Jingwen, Xi, Liting, Lin, Jiaxi, Liu, Lu, Zhang, Huixian, Wu, Airong, Xu, Chunfang, Liu, Xiaolin, Wang, Yue, Zhu, Jinzhou |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9693570/ https://www.ncbi.nlm.nih.gov/pubmed/36422105 http://dx.doi.org/10.3390/jpm12111930 |
Ejemplares similares
-
HDL-C levels added to the MELD score improves 30-day mortality prediction in Asian patients with cirrhosis
por: Wang, Yue, et al.
Publicado: (2022) -
Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals
por: Yin, Minyue, et al.
Publicado: (2022) -
Intestinal Barrier Function in the Pathogenesis of Nonalcoholic Fatty Liver Disease
por: Liu, Lu, et al.
Publicado: (2023) -
Validation of the Toronto hepatocellular carcinoma risk index for patients with cirrhosis in China: a retrospective cohort study
por: Zhang, Huixian, et al.
Publicado: (2019) -
The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study
por: Lin, Jiaxi, et al.
Publicado: (2022)