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AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:809-818. eCollection 2019.

Predictive Modeling of the Risk of Acute Kidney Injury in Critical Care: A Systematic Investigation of The Class Imbalance Problem.

Author information

Weill Cornell Medicine, Cornell University, New York, NY, USA.
Co-first authors, equal contribution.
Department of Computer Science and Engineering, Tsinghua University, Beijing, China.
Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Mayo Clinic, Rochester, MN, USA.
Corresponding Authors.


Acute Kidney Injury (AKI) in critical care is often a quickly-evolving clinical event with high morbidity and mortality. Early prediction of AKI risk in critical care setting can facilitate early interventions that are likely to provide ben- efit. Recently there have been some research on AKI prediction with patient Electronic Health Records (EHR). The class imbalance problem is encountered in such prediction setting where the number of AKI cases is usually much smaller than the controls. This study systematically investigates the impact of class imbalance on the performance of AKI prediction. We systematically investigate several class-balancing strategies to address class imbalance, includ- ing traditional statistical approaches and the proposed methods (case-control matching approach and individualized prediction approach). Our results show that the proposed class-balancing strategies can effectively improve the AKI prediction performance. Additionally, some important predictors (e.g., creatinine, chloride, and urine) for AKI can be found based on the proposed methods.


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