Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury

Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury

Objectives This study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning algorithms.Methods Patients who met the criteria for inclusion were identified in the Medical Informati...

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Bibliographic Details
Main Authors: Tianyun Gao, Zhiqiang Nong, Yuzhen Luo, Manqiu Mo, Zhaoyan Chen, Zhenhua Yang, Ling Pan
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Renal Failure
Subjects:
Sepsis
acute kidney injury
prediction model of prognosis
machine learning algorithms
Online Access:https://www.tandfonline.com/doi/10.1080/0886022X.2024.2316267
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https://www.tandfonline.com/doi/10.1080/0886022X.2024.2316267

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