A prediction and interpretation framework of acute kidney injury in critical care

Acute kidney injury (AKI) is a common clinical condition with high mortality and resource consumption. Early identification of high-risk patients to achieve an appropriate allocation of limited clinical resources and timely interventions is of significant importance, which has attracted substantial research to develop prediction models for AKI risk stratification. However, most available AKI prediction models have moderate performance and lack of interpretability, which limits their applicability in supporting care intervention. In this paper, a machine learning-based framework for AKI prediction and interpretation in critical care is presented. First, an ensemble model is developed to predict a patient's risk of AKI within 72 h of admission to the intensive care units. Next, the model is interpreted both globally and locally. For the global interpretation, the important predictors are pinpointed and the detailed relationships between AKI risk and these predictors are illustrated. For the local interpretation, patient-specific analysis is presented to provide a visualized explanation for each individual prediction. Experimental results show that such a prediction and interpretation framework can lead to good prediction and interpretation performance, which has the potential to provide effective clinical decision support.

Automated summary

A machine learning-based framework is presented for AKI prediction and interpretation in critical care, providing effective clinical decision support through global and local interpretation.