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

Kaidi Gong, Hyo Kyung Lee, Kaiye Yu, Xiaolei Xie, Jingshan Li

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number103653
JournalJournal of Biomedical Informatics
Volume113
DOIs
Publication statusPublished - 2021 Jan
Externally publishedYes

Keywords

  • Acute kidney injury (AKI)
  • Clinical decision support
  • Machine learning
  • Model interpretation
  • Prediction model

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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