A Machine Learning-Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study.

ABSTRACT:

Background

Acute kidney injury (AKI) is common in patients with community-acquired pneumonia (CAP) and is associated with increased morbidity and mortality.

Objective

This study aimed to establish and validate predictive models for AKI in hospitalized patients with CAP based on machine learning algorithms.

Methods

We trained and externally validated 5 machine learning algorithms, including logistic regression, support vector machine, random forest, extreme gradient boosting, and deep forest (DF). Feature selection was conducted using the sliding window forward feature selection technique. Shapley additive explanations and local interpretable model-agnostic explanation techniques were applied to the optimal model for visual interpretation.

Results

A total of 637

SUBMITTER: Ma M 

PROVIDER: S-EPMC11695953 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature