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Interpretable and Continuous Prediction of Acute Kidney Injury in the Intensive Care
Iacopo Vagliano, Oleksandra Lvova, Martijn C. Schut
103 - 107
10.3233/SHTI210129
Research Article
Studies in Health Technology and Informatics
Volume 281: Public Health and Informatics
Abstract

Acute kidney injury (AKI) is a common and potentially life-threatening condition, which often occurs in the intensive care unit. We propose a machine learning model based on recurrent neural networks to continuously predict AKI. We internally validated its predictive performance, both in terms of discrimination and calibration, and assessed its interpretability. Our model achieved good discrimination (AUC 0.80-0.94). Such a continuous model can support clinicians to promptly recognize and treat AKI patients and may improve their outcomes.

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