Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost

Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost

ObjectiveThe purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within 1 week of hospitalization in the intensive care unit (ICU).MethodsPatients diagnosed with DKA from the Medical Inf...

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Bibliographic Details
Main Authors: Tingting Fan, Jiaxin Wang, Luyao Li, Jing Kang, Wenrui Wang, Chuan Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-04-01
Series:Frontiers in Public Health
Subjects:
diabetic ketosis
acute kidney injury
machine learning
XGBoost
outcome
Online Access:https://www.frontiersin.org/articles/10.3389/fpubh.2023.1087297/full

Internet

https://www.frontiersin.org/articles/10.3389/fpubh.2023.1087297/full

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