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Artificial Intelligence in Predicting Kidney Function and Acute Kidney Injury

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Artificial Intelligence in Medicine

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Abstract

Acute kidney injury (AKI) is a disease defined as an abrupt decline in kidney function and is a common complication in hospitalized patients with high clinical significance. Recently, a model for predicting the onset of AKI clinical data by machine learning using electronic medical record data has attracted researchers’ attention. The state-of-the-art model has achieved high discrimination performance of area under the curve ≥0.9. Accordingly, these models are expected to be used for appropriate clinical intervention and disease prevention. In this chapter, we review the studies on AKI onset prediction and discuss their major issues. Since the event definitions, prediction timepoints, and prediction target periods used in the models widely vary, we categorize them based on previous studies. In addition, we describe various input features; algorithms, including deep learning; model performance; and recent topics such as model explainability. Finally, we summarize achievements and challenges in implementing these models as clinical decision support tools.

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Authors and Affiliations

  1. Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Eiichiro Uchino, Noriaki Sato & Yasushi Okuno

  2. Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Eiichiro Uchino & Noriaki Sato

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  1. Eiichiro Uchino
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  2. Noriaki Sato
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  3. Yasushi Okuno
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Correspondence to Yasushi Okuno .

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  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Faculty of Medicine, Imperial College London, London, UK

    Hutan Ashrafian

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Uchino, E., Sato, N., Okuno, Y. (2021). Artificial Intelligence in Predicting Kidney Function and Acute Kidney Injury. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_270-1

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