Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Wenman Zhao"'
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-12 (2024)
Abstract Background This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. Methods Population data regarding intensiv
Externí odkaz:
https://doaj.org/article/c24ca7beff284d5d83dad437bbe6d69b
Publikováno v:
BMC Nephrology, Vol 25, Iss 1, Pp 1-10 (2024)
Abstract Background The purpose of this study was to develop a nomogram for predicting in-hospital mortality in cirrhotic patients with acute kidney injury (AKI) in order to identify patients with a high risk of in-hospital death early. Methods This
Externí odkaz:
https://doaj.org/article/221aa0b27cd0406aba390a5153679474
Publikováno v:
Lipids in Health and Disease, Vol 23, Iss 1, Pp 1-8 (2024)
Abstract Background Sodium-glucose cotransporter 2 (SGLT2) inhibition is recognized for its evident renoprotective benefits in diabetic renal disease. Recent data suggest that SGLT2 inhibition also slows down kidney disease progression and reduces th
Externí odkaz:
https://doaj.org/article/cf3063ad1dca42d5b03c1964f2262a92
Publikováno v:
Renal Failure, Vol 46, Iss 1 (2024)
Background The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).Methods Af
Externí odkaz:
https://doaj.org/article/a5fc5cd40a5a4ba4be571c2dbcab9916
Autor:
Xunliang Li, Jie Zhu, Wenman Zhao, Yuyu Zhu, Li Zhu, Rui Shi, Zhijuan Wang, Haifeng Pan, Deguang Wang
Publikováno v:
Obesity Facts, Pp 1-8 (2023)
Introduction: Observational studies have shown that obesity is a risk factor for various autoimmune diseases. However, the causal relationship between obesity and autoimmune diseases is unclear. Mendelian randomization (MR) was used to investigate th
Externí odkaz:
https://doaj.org/article/79c0614d879649a6bb3d651b5b9c8791
Publikováno v:
Renal Failure, Vol 45, Iss 1 (2023)
AbstractBackground This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD).Methods This study collected data on CKD patients from 2008
Externí odkaz:
https://doaj.org/article/d38cbd8f37474953b1dad113a7ddb095
Autor:
Xunliang Li, Ruijuan Wu, Wenman Zhao, Rui Shi, Yuyu Zhu, Zhijuan Wang, Haifeng Pan, Deguang Wang
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury (SA-AKI). This study collected data on SA-AKI patients from 2008 to 2019 usi
Externí odkaz:
https://doaj.org/article/f671d1a6ee6e44a588725fd96782356f
Autor:
Guiling Liu, Xunliang Li, Wenman Zhao, Rui Shi, Yuyu Zhu, Zhijuan Wang, Haifeng Pan, Deguang Wang
Publikováno v:
Heliyon, Vol 9, Iss 8, Pp e18551- (2023)
Background: This study aimed to develop a nomogram for predicting gram-negative bacterial (GNB) infections in patients with peritoneal dialysis-associated peritonitis (PDAP) to identify patients at high risk for GNB infections. Methods: In this inves
Externí odkaz:
https://doaj.org/article/0ae1660a0a864cc1941b99cc9d5f6997
Publikováno v:
Journal of Ovarian Research, Vol 12, Iss 1, Pp 1-10 (2019)
Abstract Background Increasing researches have demonstrated the critical functions of MicroRNAs (miRNAs) in the progression of malignant tumors, including ovarian cancer. It was reported that miR-552 was an important oncogene in both breast cancer an
Externí odkaz:
https://doaj.org/article/6cbfd893ae694267bb2dca86f1707ca3
Autor:
Xunliang Li, Ruijuan Wu, Wenman Zhao, Rui Shi, Yuyu Zhu, Zhijuan Wang, Haifeng Pan, Deguang Wang
Background This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury (SA-AKI). Methods This study collected data on SA-AKI patients from 2008 t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e45701c5e6c5d3711b6c9767ec71ca0c
https://doi.org/10.21203/rs.3.rs-2217757/v1
https://doi.org/10.21203/rs.3.rs-2217757/v1
