Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease.

Journal: Renal failure

PMID: 35166177

Abstract

PURPOSE: Acute kidney injury (AKI) is a common complication and associated with a poor clinical outcome. In this study, we developed and validated a model for predicting the risk of AKI through machine learning methods in critical care patients with acute cerebrovascular disease.

Authors

Xiaohong Zhang

College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 311300, PR China.
Siying Chen

School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Kunmei Lai

Department of Nephrology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Zhimin Chen

Chengdu University of TCM, Chengdu 611137, China.
Jianxin Wan

Department of Nephrology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Yanfang Xu

Department of Nephrology, Blood Purification Research Center, the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China.

Keywords

Acute Kidney Injury Aged Cerebrovascular Disorders China Critical Care Databases, Factual Female Humans Logistic Models Machine Learning Male Middle Aged Organ Dysfunction Scores Retrospective Studies ROC Curve

External Resources

View on PubMed Access via DOI PubMed (35166177)

Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease.

Abstract

Authors

Keywords

External Resources

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