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INFORMS International Conference on Service Science

ICSS 2021: AI and Analytics for Smart Cities and Service Systems pp 76–90Cite as

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

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Part of the book series: Lecture Notes in Operations Research ((LNOR))

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.

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Acknowledgements

The project is sponsored by Tsinghua-Toyota Joint Research Institute Inter-disciplinary Program.

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Authors and Affiliations

  1. Departmentof Industrial Engineering, Tsinghua University, Beijing, 100084, China

    Kaidi Gong & Xiaolei Xie

Authors
  1. Kaidi Gong
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  2. Xiaolei Xie
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Corresponding author

Correspondence to Xiaolei Xie .

Editor information

Editors and Affiliations

  1. Division of Engineering and Information Science, Pennsylvania State University, Malvern, PA, USA

    Robin Qiu

  2. Faculty of Information (Cross-appointed to Computer Science), Faculty Affiliate, Schwartz Reisman Institute, University of Toronto, Toronto, ON, Canada

    Kelly Lyons

  3. Department of Supply Chain Management, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

    Weiwei Chen

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Gong, K., Xie, X. (2021). An Interpretable Ensemble Model of Acute Kidney Disease Risk Prediction for Patients in Coronary Care Units. In: Qiu, R., Lyons, K., Chen, W. (eds) AI and Analytics for Smart Cities and Service Systems. ICSS 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-030-90275-9_7

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