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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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Joana Gameiro
Administração Regional de Saúde de Lisboa e Vale do Tejo
Tiago Branco
Algarve Biomedical Center
José António Lopes
University of Lisbon
Journal of Clinical Medicine
Centro Hospitalar Lisboa Norte
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Gameiro et al. (Tue,) studied this question.
synapsesocial.com/papers/6a165426a5fa93de3db8e32f — DOI: https://doi.org/10.3390/jcm9030678