Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on the MIMIC database.

ABSTRACT:

Objective

This study developed and validated a stacked ensemble machine learning model to predict the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis.

Design

A retrospective study based on patient data from public databases.

Participants

This study analysed 1295 patients with acute pancreatitis complicated by septicaemia from the US Intensive Care Database.

Methods

From the MIMIC database, data of patients with acute pancreatitis and sepsis were obtained to construct machine learning models, which were internally and externally validated. The Boruta algorithm was used to select variables. Then, eight machine learning algorithms were used to construct prediction models for acute kidney injury (AKI) occurrence in intensive ca

SUBMITTER: Li F 

PROVIDER: S-EPMC11865797 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature