Construction and validation of prognostic models for acute kidney disease and mortality in patients at risk of malnutrition: an interpretable machine learning approach.

ABSTRACT:

Background

Acute kidney injury (AKI) is a prevalent complication in patients at risk of malnutrition, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. This study aimed to develop and validate machine learning (ML) models for predicting the occurrence of AKD, AKI and mortality in patients at risk of malnutrition.

Methods

We retrospectively reviewed the medical records of patients at risk of malnutrition. Eight ML algorithms were employed to predict AKD, AKI and mortality. The performance of the best model was evaluated using various metrics and interpreted using the SHapley Additive exPlanation (SHAP) method. An artificial intelligence (AI)-driven web application was also created based on the best model.

SUBMITTER: Wang X 

PROVIDER: S-EPMC11997651 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature