Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease
Background 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 to 2019 using the Medical Information Mart for Intensive C...
Saved in:
| Main Authors: | Xunliang Li, Yuyu Zhu, Wenman Zhao, Rui Shi, Zhijuan Wang, Haifeng Pan, Deguang Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
|
| Series: | Renal Failure |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2023.2212790 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Development and validation of a nomogram for predicting acute kidney injury in elderly patients in intensive care unit
by: Li Zhao, et al.
Published: (2025-12-01) -
Prediction of mortality with unmeasured anions in critically ill patients on mechanical ventilation
by: Novović Miloš N., et al.
Published: (2014-01-01) -
Impact of New-Onset Atrial Fibrillation on Mortality in Critically Ill Patients
by: Zhang HD, et al.
Published: (2024-11-01) -
Prognostic Scores for Acute Kidney Injury in Critically Ill Patients
by: Wisble Pereira Sousa, et al.
Published: (2024-11-01) -
Hospitalization of very old critically ill patients in medical intermediate care units in France: a nationwide population-based study
by: Adrien Migeon, et al.
Published: (2025-05-01)