Development and deployment of interpretable machine-learning model for predicting in-hospital mortality in elderly patients with acute kidney disease-Reference-Cited by-同舟云学术

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

Published:2022-11-07 Issue:1 Volume:44 Page:1896-1906
ISSN:0886-022X
Container-title:Renal Failure
language:en
Short-container-title:Renal Failure

Author:

Li Mingxia¹^ORCID,Zhuang Qinghe²,Zhao Shuangping¹³⁴,Huang Li¹,Hu Chenghuan¹,Zhang Buyao¹,Hou Qinlan¹

Affiliation:

1. Department of Critical Care Medicine, Xiangya Hospital Central South University, Changsha, China

2. School of Computer Science and Engineering, Central South University, Changsha, China

3. National Clinical Research Center for Geriatric Disorders, Changsha, China

4. Hunan Provincial Clinical Research Center of Intensive Care Medicine, Changsha, China

Publisher

Informa UK Limited

Subject

Nephrology,Critical Care and Intensive Care Medicine,General Medicine

Link

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

Reference39 articles.

1. Clinical Decision Support for In-Hospital AKI

2. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study

3. Acute kidney injury

4. Acute kidney injury in elderly patients: narrative review on incidence, risk factors, and mortality

5. Incidence, risk factors and prognostic factors of acute renal failure in patients admitted to an intensive care unit

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients;Frontiers in Medicine;2024-08-15

2. Using artificial intelligence to predict mortality in AKI patients: a systematic review/meta-analysis;Clinical Kidney Journal;2024-05-17

3. The Correlation between Changes in Serum Sodium and Hospital Mortality in Critically Ill Children with Acute Kidney Injury;Journal of Medicine and Health Science;2024-03

4. Machine Learning-Based Monitoring and Prognosis of Chronic Kidney Disease Patients;2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI);2023-12-29

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