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Comment
. 2019 Nov;15(11):663-664.
doi: 10.1038/s41581-019-0203-y.

Artificial intelligence to predict AKI: is it a breakthrough?

John A Kellum  1 , Azra Bihorac  2
Affiliations
Comment

Artificial intelligence to predict AKI: is it a breakthrough?

John A Kellum et al. Nat Rev Nephrol. 2019 Nov.

Abstract

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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Figures

Fig. 1 |
Fig. 1 |. Implementation of deep learning algorithms to identify patients at high risk of AKI.
Deep learning algorithms developed to support clinical decisions in real time should be based on integrated patient information, including electronic health records (EHRs) with detailed medical history (including ongoing problems and procedures), physiological parameters (such as vital signs and laboratory results) and medication details. Acute kidney injury (AKI) risk scores derived from such an algorithm would stratify patients and inform clinical decisions, including the use of additional diagnostics to enable personalized treatment.
See this image and copyright information in PMC

Comment on

  • A clinically applicable approach to continuous prediction of future acute kidney injury.
    Tomašev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, Mottram A, Meyer C, Ravuri S, Protsyuk I, Connell A, Hughes CO, Karthikesalingam A, Cornebise J, Montgomery H, Rees G, Laing C, Baker CR, Peterson K, Reeves R, Hassabis D, King D, Suleyman M, Back T, Nielson C, Ledsam JR, Mohamed S. Tomašev N, et al. Nature. 2019 Aug;572(7767):116-119. doi: 10.1038/s41586-019-1390-1. Epub 2019 Jul 31. Nature. 2019. PMID: 31367026 Free PMC article.

References

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    1. Tomasev N et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572, 116–119 (2019). - PMC - PubMed
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    1. Kellum JA & Prowle JR Paradigms of acute kidney injury in the intensive care setting. Nat. Rev. Nephrol 14, 217–230 (2018). - PubMed

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