Machine Learning-Aided Decision-Making Model for the Discontinuation of Continuous Renal Replacement Therapy.

Journal: Blood purification

PMID: 38865971

Abstract

INTRODUCTION: Continuous renal replacement therapy (CRRT) is a primary form of renal support for patients with acute kidney injury in an intensive care unit. Making an accurate decision of discontinuation is crucial for the prognosis of patients. Previous research has mostly focused on the univariate and multivariate analysis of factors in CRRT, without the capacity to capture the complexity of the decision-making process. The present study thus developed a dynamic, interpretable decision model for CRRT discontinuation.

Authors

Siyi Zhu

Department of Computer Science, Xiamen University, Xiamen 361005, China.
Jing Yan

Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, China.
Shijin Gong

Department of Critical Care Medicine, Zhejiang Hospital, Hangzhou, China.
Xue Feng

Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia.
Gangmin Ning

College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, P.R.China.
Liang Xu

Keywords

Acute Kidney Injury Aged Algorithms Clinical Decision-Making Continuous Renal Replacement Therapy Female Humans Intensive Care Units Machine Learning Male Middle Aged Renal Replacement Therapy

External Resources

View on PubMed Access via DOI PubMed (38865971)

Machine Learning-Aided Decision-Making Model for the Discontinuation of Continuous Renal Replacement Therapy.

Abstract

Authors

Keywords

External Resources

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