Machine learning-based prognostic prediction model of pneumonia-associated acute respiratory distress syndrome.

ABSTRACT:

Objective

This study aimed to construct a machine learning predictive model for prognostic analysis of patients with p- ARDS.

Methods

In this single-center retrospective study, 230 patients with p- ARDS admitted to the RICU of the second affiliated hospital of Chongqing Medical University from January 2020 to November 2024 were included. Patients were divided into survival group and death group according to the 28-day prognosis results. All patients' clinical data were first results within 24 h of admission. 20% of the total samples were randomly selected as the test set, and the remaining samples were used as the training set for crossvalidation, and six different models were constructed, including Logistic Regression, Random Forest, NaiveBayes, SVM, XGBoost and Adaboost. T

SUBMITTER: Lv J 

PROVIDER: S-EPMC12268496 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature