Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury

Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury

AbstractBackground Although current guidelines didn’t support the routine use of furosemide in oliguric acute kidney injury (AKI) management, some patients may benefit from furosemide administration at an early stage. We aimed to develop an explainable machine learning (ML) model to differentiate be...

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Bibliographic Details
Main Authors: Meng Jiang, Chun-qiu Pan, Jian Li, Li-gang Xu, Chang-li Li
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:Renal Failure
Subjects:
Oliguric acute kidney injury
furosemide responsiveness
machine learning
XGBoost modeling
Online Access:https://www.tandfonline.com/doi/10.1080/0886022X.2022.2151468

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https://www.tandfonline.com/doi/10.1080/0886022X.2022.2151468

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