Artificial Intelligence in Acute Kidney Injury Risk Prediction

Journal of Clinical Medicine
Joana GameiroJose Antonio Lopes

Abstract

Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is associated with worse short and long-term outcomes. It is crucial to develop methods to identify patients at risk for AKI and to diagnose subclinical AKI in order to improve patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed for the development of artificial intelligence predictive models of risk estimation in AKI. In this review, we discussed the progress of AKI risk prediction from risk scores to electronic alerts to machine learning methods.

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Citations

Apr 17, 2020·Journal of Clinical Medicine·Charat ThongprayoonWisit Cheungpasitporn
Jun 4, 2020·Journal of Clinical Medicine·Karolina WoziwodzkaKatarzyna Krzanowska
Aug 23, 2020·Journal of Clinical Medicine·Joana GameiroJosé António Lopes
Oct 13, 2020·Critical Reviews in Clinical Laboratory Sciences·Sander De BruyneJoris R Delanghe
Dec 19, 2020·Journal of Biomedical Informatics·Kaidi GongJingshan Li
May 29, 2021·Scientific Reports·Jill VanmassenhoveWim Van Biesen
Jul 29, 2021·Indian Journal of Critical Care Medicine : Peer-reviewed, Official Publication of Indian Society of Critical Care Medicine·Swagata Tripathy
Sep 25, 2021·Clinical Chemistry·Daniel S HermanThomas J S Durant

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AKIpredictor
MySurgeryRisk

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