Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

ABSTRACT:

Objective

The objective was to establish a machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

Methods

80% of sepsis patients selected randomly from the MIMIC-IV database, without prior pulmonary conditions and with nonpulmonary infection sites, were used to construct prediction models through machine learning techniques (including K-nearest neighbour, extreme gradient boosting, support vector machine, deep neural network, and decision tree methods). The remaining 20% of patients were utilized to validate the model's accuracy. Additionally, local data were employed for further model validation.

Results

A total of 11,409 patients were included, with the most common type of infection being bloodstream infectio

SUBMITTER: Lin J 

PROVIDER: S-EPMC11568264 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature