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External validation of models for the prediction of acute kidney injury (AKI) is rare. We externally validate AKI prediction models in intensive care units. The models were developed on the Medical Information Mart for Intensive Care dataset and validated on the eICU dataset. Traditional machine learning models show limited transportability to the new population (AUROC < 0.8). Models based on recurrent neural networks, which can capture complex relationships between the data, transport well to the new population (AUROC 0.8-0.9). Such models can help clinicians to recognize AKI and improve the outcome.

Acute kidney injury (AKI) is an abrupt decrease of kidney function which is common in the intensive care. Many AKI prediction models have been proposed, but an analysis of what is the added value of clinical notes and medical terminologies has not yet been conducted. We developed and internally validated a model to predict AKI that includes not only clinical variables, but also clinical notes and medical terminologies. Our results were overall good (AUROC > 0.80). The best model used only clinical variables (AUROC 0.899).