Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy.

ABSTRACT:

Background

There are many Machine Learning (ML) models which predict acute kidney injury (AKI) for hospitalised patients. While a primary goal of these models is to support clinical decision-making, the adoption of inconsistent methods of estimating baseline serum creatinine (sCr) may result in a poor understanding of these models' effectiveness in clinical practice. Until now, the performance of such models with different baselines has not been compared on a single dataset. Additionally, AKI prediction models are known to have a high rate of false positive (FP) events regardless of baseline methods. This warrants further exploration of FP events to provide insight into potential underlying reasons.

Objective

The first aim of this study was to assess the variance in performa

SUBMITTER: Kamel Rahimi A 

PROVIDER: S-EPMC10563357 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature