An Interpretable Ensemble Model of Acute Kidney Disease Risk Prediction for Patients in Coronary Care Units

Kaidi Gong and Xiaolei Xie ()
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Kaidi Gong: Departmentof Industrial Engineering, Tsinghua University
Xiaolei Xie: Departmentof Industrial Engineering, Tsinghua University

A chapter in AI and Analytics for Smart Cities and Service Systems, 2021, pp 76-90 from Springer

Abstract: Abstract Acute kidney injury (AKI), which refers to the abrupt decrease in renal function, is a common clinical condition in cardiovascular patients. The interaction between heart and kidney complicates the patient's condition, increasing the difficulty of treatment. Therefore, AKI is one of the focuses in the management of cardiovascular patients. Timely reversal of AKI is beneficial to the treatment of patients. In this study, we build a voting-based ensemble prediction model of acute kidney disease (AKD, referring to acute or subacute kidney damage for 7–90 days) risk for patients in coronary care units (CCU) based on clinical data within the first 6 h since CCU admission, in order to identify the patients whose AKI is hard to reverse. Then we interpret our prediction model using Shapley additive explanation values to help pinpoint important predictors and influence of clinical characteristics such as fluid status on AKD risk. The results show that our method has the potential to support clinical decision making.

Keywords: Acute kidney injury; Machine learning; Model interpretation; Clinical decision support (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-030-90275-9_7

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DOI: 10.1007/978-3-030-90275-9_7

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