Explainable AI identifies key biomarkers for acute kidney injury prediction in the ICU.

ABSTRACT:

Background

Early identification of acute kidney injury (AKI) in the intensive care unit (ICU) remains challenging. We aimed to identify key predictors of new-onset AKI within 48 h after ICU admission and renal replacement therapy (RRT) need within 7 days, using explainable artificial intelligence (XAI) with eXtreme Gradient Boosting (XGBoost). We also assessed whether XGBoost improved predictive performance.

Methods

A retrospective cohort study across four ICUs was conducted as part of the SWECRIT biobank project. Blood samples were prospectively obtained at ICU admission and retrospectively analysed. AKI was defined by the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. XAI models were compared with logistic regression models, incorporating emerging biomarkers a

SUBMITTER: Koozi H 

PROVIDER: S-EPMC12545979 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature