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

可解释性 急性肾损伤 口译(哲学) 重症监护医学 心理干预 预测建模 医学 计算机科学 危险分层 临床决策支持系统 机器学习 人工智能 风险分析(工程) 决策支持系统 内科学 程序设计语言 精神科
作者
Kaidi Gong,Hyo Kyung Lee,Kaiye Yu,Xiaolei Xie,Jingshan Li
出处
期刊:Journal of Biomedical Informatics [Elsevier]
卷期号:113: 103653-103653 被引量:21
标识
DOI:10.1016/j.jbi.2020.103653
摘要

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.

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