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ACUTE KIDNEY INJURY

Artificial intelligence to predict AKI: is it a breakthrough?

Nature Reviews Nephrology volume 15, pages 663–664 (2019)Cite this article

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A new study of deep learning based on electronic health records promises to forecast acute kidney injury up to 48 hours before it can be diagnosed clinically. However, employing data science to predict acute kidney injury might be more challenging than it seems.

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Fig. 1: Implementation of deep learning algorithms to identify patients at high risk of AKI.

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Acknowledgements

A.B. is supported by R01 GM110240 from the National Institute of General Medical Sciences.

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Authors and Affiliations

  1. Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    John A. Kellum

  2. Precision and Intelligent Systems in Medicine (PrismaP), Department of Medicine, University of Florida, Gainesville, FL, USA

    Azra Bihorac

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  1. John A. Kellum
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  2. Azra Bihorac
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Correspondence to John A. Kellum.

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Competing interests

J.A.K. received honoraria for consulting and grant support from Astute Medical, Biomerieux and Bioporto. A.B. and University of Florida have patents pending on the real-time use of clinical data for risk prediction of sepsis-associated and surgery-associated AKI using machine learning models.

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Kellum, J.A., Bihorac, A. Artificial intelligence to predict AKI: is it a breakthrough?. Nat Rev Nephrol 15, 663–664 (2019). https://doi.org/10.1038/s41581-019-0203-y

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