Automated machine learning for early prediction of acute kidney injury in acute pancreatitis.

ABSTRACT:

Background

Acute kidney injury (AKI) represents a frequent and grave complication associated with acute pancreatitis (AP), substantially elevating both mortality rates and the financial burden of hospitalization. The aim of our study is to construct a predictive model utilizing automated machine learning (AutoML) algorithms for the early prediction of AKI in patients with AP.

Methods

We retrospectively analyzed patients who were diagnosed with AP in our hospital from January 2017 to December 2021. These patients were randomly allocated into a training set and a validation set at a ratio of 7:3. To develop predictive models for each set, we employed the least absolute shrinkage and selection operator (LASSO) algorithm along with AutoML. A nomogram was developed based on multi

SUBMITTER: Zhang R 

PROVIDER: S-EPMC10785491 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature