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.

急性肾损伤（AKI）是常见的临床疾病，死亡率高，资源消耗大。尽早识别高危患者以适当分配有限的临床资源并及时采取干预措施具有重要意义，这吸引了大量研究来开发AKI风险分层的预测模型。但是，大多数可用的AKI预测模型性能中等且缺乏可解释性，这限制了它们在支持护理干预方面的适用性。本文提出了一种基于机器学习的重症监护中AKI预测和解释框架。首先，建立整体模型来预测重症监护病房入院72小时内患者发生AKI的风险。接下来，模型将在全局和本地解释。对于全局解释，要指出重要的预测因素，并说明AKI风险与这些预测因素之间的详细关系。对于局部解释，提出了针对患者的分析，以为每个单独的预测提供可视化的解释。实验结果表明，这种预测和解释框架可以带来良好的预测和解释性能，有可能提供有效的临床决策支持。