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. 2020 Jan 14;7:536. doi: 10.3389/fped.2019.00536

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Copyright © 2020 Sutherland.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Automated, Real-time AKI Identification. In this figure, temporal creatinine (daily) and urine output (hourly) trends are displayed. In (A), the creatinine gradually increases from a baseline of 0.6 mg/dL, meeting AKI criteria (serum creatinine > 1.5× baseline) on 1/5/19. Likewise, in (B), the patient develops progressive oliguria, meeting AKI criteria (UOP < 0.5 mL/kg/h for 6 h) at 15:00. In both cases, the ability to detect the threshold value upon documentation allows real-time diagnosis. This, in turn, opens up a myriad of big data AKI solutions.