Development and deployment of interpretable machine-learning model for predicting in-hospital mortality in elderly patients with acute kidney disease

Development and deployment of interpretable machine-learning model for predicting in-hospital mortality in elderly patients with acute kidney disease

Background Acute kidney injury (AKI) is more likely to develop in the elderly admitted to the intensive care unit (ICU). Acute kidney disease (AKD) affects ∼45% of patients with AKI and increases short-term mortality. However, there are no studies on the prognosis of AKD in the elderly.Methods Data...

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Bibliographic Details
Main Authors: Mingxia Li, Qinghe Zhuang, Shuangping Zhao, Li Huang, Chenghuan Hu, Buyao Zhang, Qinlan Hou
Format: Article
Language:English
Published: Taylor & Francis Group 2022-12-01
Series:Renal Failure
Subjects:
Acute kidney disease
in-hospital mortality
elderly patients
machine learning
noninvasive parameters
Online Access:https://www.tandfonline.com/doi/10.1080/0886022X.2022.2142139

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https://www.tandfonline.com/doi/10.1080/0886022X.2022.2142139

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