Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease-Reference-Cited by-同舟云学术

Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease

Published:2023-05-19 Issue:1 Volume:45 Page:
ISSN:0886-022X
Container-title:Renal Failure
language:en
Short-container-title:Renal Failure

Author:

Li Xunliang¹²,Zhu Yuyu¹²,Zhao Wenman¹²^ORCID,Shi Rui¹²,Wang Zhijuan¹²,Pan Haifeng²³⁴,Wang Deguang¹²

Affiliation:

1. Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, People’s Republic of China

2. Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, People’s Republic of China

3. Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, People’s Republic of China

4. Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, People’s Republic of China

Funder

Natural Science Foundation of Anhui Province

Incubation Program of National Natural Science Foundation of China of The Second Hospital of Anhui Medical University

Clinical Research Incubation Program of The Second Hospital of Anhui Medical University

Co-construction Project of Clinical and Preliminary Disciplines of Anhui Medical University in 2020

Co-construction Project of Clinical and Preliminary Disciplines of Anhui Medical University in 2021

Publisher

Informa UK Limited

Subject

Nephrology,Critical Care and Intensive Care Medicine,General Medicine

Link

https://www.tandfonline.com/doi/pdf/10.1080/0886022X.2023.2212790

Reference33 articles.

1. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the global burden of disease study 2017;GBD Chronic Kidney Disease Collaboration;Lancet,2020

2. Hypoxia-inducible factor signaling in vascular calcification in chronic kidney disease patients

3. Outcomes of Chronic Dialysis Patients Admitted to the Intensive Care Unit

4. Case mix, outcome and activity for patients admitted to intensive care units requiring chronic renal dialysis: a secondary analysis of the ICNARC Case Mix Programme Database

5. Structural and Functional Changes With the Aging Kidney

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